Tuberculosis Detection Using Deep Learning | Tuberculosis Classification Using Python Projects With Source Code | Final Year Major Projects

ABSTRACT

          Lung problems encompass an array of lung disorders, including asthma, TB, lung disease, and numerous other respiratory disorders. For many years, tuberculosis has been a significant public health issue. TB is usually diagnosed with chest X-rays, which are essential tools for screening and detecting the disease. In spite of this, accurate diagnosis of TB using chest X-rays is challenging due to the complexity of the disease and the variation in how it appears on images. In medical image analysis, including in TB recognition in chest, deep learning techniques have been widely used. This project is developed in python using deep learning approach. 

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Sugarcane Leaf Disease Detection Using Machine Learning | Sugarcane Plant Disease Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

                    Now-a-days wheat plants are getting infected by different types of diseases very rapidly. It is must to come up with new system to single out diseases. It is must to design and implement such a system that can easily find out the diseases infected by plants. In India many crops are cultivated, out of which wheat being one of the most important food grain that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India’s economy. Hence, maintenance of the steady production of above stated crop is very important. The main idea of this project is to provide a system for detecting sugarcane leaf diseases. The given system will find the disease on leaf image of a sugarcane plant through image processing this project is develop in matlab. Former algorithms are used for extracting vital information from the leaf and the latter is used for detecting the disease that it is infected with. This Project is developed in matlab using machine learning.

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Image Encryption And Decryption Using DNA Algorithm | DNA Based Image Encryption Decryption Using Matlab Projects With Source Code | Final Year Projects

ABSTRACT

      The development of a new image encryption algorithm using real structures of deoxyribonucleic acid (DNA) molecules is considered. In the proposed algorithm, the encryption process is performed by confusing and rearranging the pixels of the image based on the coordinates of the chaotic points obtained by the chaos game of DNA symbols, the sequence of DNA symbols, and the encoding rule. We propose a new image encryption algorithm based on DNA sequences combined with chaotic maps. This algorithm has innovations it diffuses the pixels by transforming the nucleotides into corresponding base pairs a random number of times. For any size of the original grayscale image, the rows and columns are fist exchanged by the arrays generated each pixel that has been confused is encoded into four nucleotides according to the DNA coding each nucleotide is transformed into the corresponding base pair a random number of time(s) by a series of iterative computations. This project is developed in matlab.

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Lumpy Disease Detection Using Image Processing | Lumpy Skin Classification Using Matlab Project Source Code | Final Year Major Project

ABSTRACT

          Animal illness is now a widespread problem. sickness identification is essential because there are various sorts of sickness in creatures, and the opinion will be delivered in a timely manner. Cows with the Neethling infection develop lumpy skin complaints. The affection of these illnesses causes lasting harm to the cattle's skin. Reduced milk production, gravidity, poor growth, revocation, and, in severe cases, mortality, are the most common effects of the illness. We developed a deep learning-based architecture that can predict or detect disease. To discover the pathogen that causes lumpy skin problem, it is crucial to employ a deep literacy system. The virus (LSDV) that causes lumpy skin disease can infect cattle. Ticks and other animals that feed on blood, such as flies, mosquitoes, and ticks, spread it. Animals who have never been exposed to the disease may develop nodes on their skin, a fever, and even pass away as a result of it. Two methods of control are vaccinations and rewarding afflicted creatures. The purpose of this study was to evaluate how well some deep learning algorithms could understand the context of an infection causing a Lumpy Skin complaint. This project is developed in matlab using image processing.

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Prof. Roshan P. Helonde

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Currency Recognition Using Deep Learning CNN | Currency Classification Using Python Project With Source Code | Final Year Major Project

ABSTRACT

             The Reserve Bank is the one which issue bank notes in India. Reserve Bank, changes the design of bank notes from time to time. The appearance of the currency is part of this development and it is affected directly, where there is exploited in incorrect form by copying the currency in a manner similar to the reality. Therefore, it became necessary to implement a proposal for being a suitable as solution not inconsistent with the different cultures, time and place. This clear through add the watermarks inside currency, which is difficult to be copied. At the same time, this watermarks may be visible to the naked eye so can easily inferred or it is invisible. However the high resolution imaging devices can copy these additions. In this research, we have proposed a system to distinguish the currencies by the program that working a submission inferred to the watermark by feature extraction determined the type of currency. In addition to, it determined category of the currency. Benefit of it, is reducing as much as possible the spread of counterfeit currency and this system can be used by any user wants to make sure of the currency. This project is developed in python using deep learning cnn.

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Banana Leaf Disease Detection Using CNN | Banana Plant Disease Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

             Disease diagnosis and classification in banana crop using image processing technique is an interesting and useful application for farmers to identify, analyze and manage plant pathogens within fields as effectively and automatically at minimum cost. Major banana diseases express their symptoms on leaf area in their earlier stage of infection. These disease can be analyzed and classified automatically through computer vision and machine vision systems that use image processing techniques for information interpretation. This project shows various disease identified on banana plant leaf using cnn convolutional neural network. This project is develop in matlab.

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Visible Image Watermarking Using DWT Technique | Visible Image Watermarking Using Python Project With Source Code | Final Year Major Projects

ABSTRACT

           Digital watermarking is a technology for embedding various types of information in digital content. In general, information for protecting copyrights and proving the validity of data is embedded as a watermark. A digital watermark is a digital signal or pattern inserted into digital content. The digital content could be a still image, an audio clip, a video clip, a text document, or some form of digital data that the creator or owner would like to protect. The main purpose of the watermark is to identify who the owner of the digital data is, but it can also identify the intended recipient. The Image watermarking is most popular method for copyright protection by discrete Wavelet Transform which performs two level decomposition of original cover image and watermark image is embedded in lowest level sub band of cover image. Inverse Discrete Wavelet Transform is used to recover original image from watermarked image. This project is developed in python.

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Prof. Roshan P. Helonde

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Traffic Sign Recognition Using Deep Learning | Traffic Sign Classification Using Python Project With Source Code | Final Year Major Project Code

ABSTRACT

               Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature exactor and classifier. Current popular algorithms mainly use deep learning cnn to execute feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in cnn form a classical neural network classifier, which is trained by conventional gradient descent-based implementations, the generalization ability is limited. The performance could be further improved if other favorable classifiers are used in python with accuracy of upto 98 %.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

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Skin Disease Detection Using Deep Learning | Skin Disease Classification Using Matlab Project With Source Code | Final Year Major Projects

ABSTRACT

          Skin diseases are more common than other diseases. Skin diseases may be caused by fungal infection, bacteria, allergy, or viruses, etc. A skin disease may change texture or color of the skin. In general, skin diseases are chronic, infectious and sometimes may develop into skin cancer. The advancement of lasers and Photonics based medical technology has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases. Computer vision has a role in the detection of skin diseases in a variety of techniques. Due to deserts and hot weather, skin diseases are common in various country. We proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area, then use image analysis to identify the type of skin disease. This project is developed in matlab using deep learning techniques.

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Skin Cancer Classification Using CNN Convolutional Neural Network | Skin Cancer Detection Using Python Project With Source Code | IEEE Project with Source Code

ABSTRACT

                Many of the skin diseases are very dangerous, particularly if not treated at an early stage. Skin diseases are becoming common because of the increasing pollution. Skin diseases tend to pass from one person to another. Human habits tend to assume that some Melanoma Skin Cancer are not serious problems. Sometimes, most of the people try to treat these infections of the skin using their own method. However, if these treatments are not suitable for that particular skin problem then it would make it worse. And also sometimes they may not be aware of the dangerous of their Melanoma Skin Cancer, for instance skin cancers. With advance of medical imaging technologies, the acquired data information is getting so rich toward beyond the human’s capability of visual recognition and efficient use for clinical assessment. In this project we propose a diagnosis system which will enable users to detect and recognize skin diseases with the help of image processing and provide the user advises or treatments based on the results obtained in a shorter time period than the existing methods. In this project, we will be constructing a diagnosis system based on the techniques of Image Processing. We will be making use of Python to perform the pre-processing and processing of the skin images of the users. This processing will be conducted on the different skin patterns and will be analyzed to obtain the results from which we can identify which skin disease the user is suffering from. This data will help in early detection of the skin diseases and in providing their cure. Through this we will be finding a cost effective and feasible test method for the detection of skin disorders. The results obtained will be classified according to the given prototype and diagnosis accuracy assessment will be performed to provide users with efficient and fast results.

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Prof. Roshan P. Helonde

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Paddy Leaf Disease Detection Using Image Processing | Paddy Plant Disease Classification Using Matlab Project With Source Code

ABSTRACT

           Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect, classify the diseases in paddy leafs. Paddy leaf diseases detection done using image processing and cnn techniques. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging. The spread of plant pests and diseases has increased dramatically in recent years. Globalization, trade and climate change, as well as reduced resilience in production systems due to decades of agricultural intensification, have all played a part. Plant pathogens can be fungal, bacterial, viral or nematodes and can damage plant parts above or below the ground. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Vegetable Leaf Classification Using Deep Learning Matlab Project | Vegetable Plant Recognition Using Matlab Project With Source Code

ABSTRACT

             Leaf Recognition is now emerging for research purposes. Leaf recognition technology plays an important role in plant classification and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. It is well known that the correct way to extract plant features involves plant recognition based on leaf images. In Agriculture, vegetables plants have become an important source of energy and source of living for farmers. Correctly identifying a vegetable leaf allows farmers to differentiate between vegetables as well as a vegetable seedling and weed in the garden. With so many varieties of leafy greens coming from our local farmers each week, it can be difficult to figure out vegetable it belongs to. Though these leaves may appear similar at a glance, they are actually quite unique in terms of Shape, Texture and Color. And with the increasing use of innovative computer technology, digitalized ways have become a possibility for plant identification. The proposed system will solve the problem of determining the vegetables just through the photograph of their leaves. In particular, identification process is carried out by gathering leaves detached from the plants, treated and stained prior to the imaging. Recognition of Vegetable Leaf using Matlab project, is to create an Informative Vegetable’s Leaf Recognition using Matlab to help the farmers, botanist and Agricultural Researchers in identifying a vegetable and its common details in a convenient and reliable way. The output parameters are used to compute well documented metrics for the statistical and shape. Base on the study, the following conclusion are drawn: The system can extract various parameters from the leaf’s image that will be used in identifying Vegetable`s from the extracted leaf parameters, the system provides the statistical analysis and general information of the identified leaf. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Lumpy Disease Detection Using Image Processing | Lumpy Skin Classification Using Python Project Source Code | Final Year Project

ABSTRACT

          Animal illness is now a widespread problem. sickness identification is essential because there are various sorts of sickness in creatures, and the opinion will be delivered in a timely manner. Cows with the Neethling infection develop lumpy skin complaints. The affection of these illnesses causes lasting harm to the cattle's skin. Reduced milk production, gravidity, poor growth, revocation, and, in severe cases, mortality, are the most common effects of the illness. We developed a deep learning-based architecture that can predict or detect disease. To discover the pathogen that causes lumpy skin problem, it is crucial to employ a deep literacy system. The virus (LSDV) that causes lumpy skin disease can infect cattle. Ticks and other animals that feed on blood, such as flies, mosquitoes, and ticks, spread it. Animals who have never been exposed to the disease may develop nodes on their skin, a fever, and even pass away as a result of it. Two methods of control are vaccinations and rewarding afflicted creatures. The purpose of this study was to evaluate how well some deep learning algorithms could understand the context of an infection causing a Lumpy Skin complaint. This project is developed in python using image processing.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Foot Ulcer Detection Using Image Processing Using Matlab | Foot Ulcer Classification Using Matlab Project With Source Code

ABSTRACT

         Now a days, diabetic patient are more prone to have foot ulcer. Diabetes affects the foot generally by two ways, such as breaking of nerves and weakening of blood vessels. Strange increase of glucose in the blood results in nerve injury, termed as diabetic neuropathy. Lack of sensation and hardening of the foot may indicate nerve injury. Due to this, diabetic patients would not sense a small wound or an inflammation on foot. Lack of sensation in the foot may cause abnormal walking and standing. Flattered arches fractures and non-healing blisters may occur due to improper balance of foot. Diabetes also has an effect on blood vessels. Thin blood vessels carry a lesser amount of blood to the foot. Oxygen is conceded to the blood cell. When the blood vessels are tapering, less blood and oxygen reaches the foot. This can delay wound healing. Insufficient blood will not carry sufficient oxygen and nutrient to the foot for healing and fight against infection. Gangrene may also affect the diabetic patient due to lack of blood supply. Treatment may require an amputation of the foot. The infected part of the foot was removed surgically. Initial finding, care and treatment can avoided the need of amputation. Symptoms such as redness, swelling, and increase temperature are the indication of foot ulcer. In this project an image processing technique is proposed to identify weather ulcer is healing or not. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Image Encryption Decryption Using AES Algorithm | Secret Key Based Image Encryption Decryption Using Python Project Source Code | Final Year Project

ABSTRACT

            In the last few years the security and integrity of the data is the most important concern. Now a day’s almost all the data is transferred over the computer networks and it has increased the attacks over the network. Before transmitted data it has to be encrypted and store so that it cannot be attacked by various attackers. Encryption is a process of hiding the data. During the last decade information security has become the major issue. As there is rapid growth of using images in many fields, so it is important to protect the private image data from the intruders. Image protection has become an imperative issue. To protect an individual privacy has become a crucial task. Different methods have been explore and developed to preserve data and personal information. To protect the important information from unauthorized users, image encryption is used. Encryption is the one of the most used technique to hidden the data from unauthorized users. The encrypting and decrypting of the data has been widely investigated because the demand for the better encryption and decryption of the data is gradually increased for getting the better security for the communication between the devices more privately. The cryptography play a major role for the fulfillment for this demand. The purpose of this project is to provide the better as well as more secure communication system by enhancing the strength of Advance Encryption Standard (AES) algorithm. AES algorithm was known for providing the best security without any limitations. This project is developed in python.

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Prof. Roshan P. Helonde

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Wheat Plant Disease Classification Using Deep Learning | Wheat Leaf Disease Detection Using Matlab Project With Source Code | Final Year Project

ABSTRACT

                    Now-a-days wheat plants are getting infected by different types of diseases very rapidly. It is must to come up with new system to single out diseases. It is must to design and implement such a system that can easily find out the diseases infected by plants. In India many crops are cultivated, out of which wheat being one of the most important food grain that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India’s economy. Hence, maintenance of the steady production of above stated crop is very important. The main idea of this project is to provide a system for detecting wheat leaf diseases. The given system will find the disease on leaf image of a wheat plant through image processing this project is develop in python. Former algorithms are used for extracting vital information from the leaf and the latter is used for detecting the disease that it is infected with. This Project is developed in matlab using deep learning.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

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Tomato Leaf Disease Classification Using CNN | Tomato Leaf Disease Detection Using Matlab Project with Source Code | IEEE Based Project

ABSTRACT

          India is an agricultural country and tomato production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of tomato plant diseases is essential to detect the symptoms of tomato diseases as early as they appear on the growing stage. This project proposed a methodology for the analysis and detection of tomato plant leaf diseases using recent digital image processing techniques. In this project, experimental results demonstrate that the proposed method can successfully detect and classify the major tomato leaf diseases like Bacterial Spot, Blight Disease, Leaf Curl Virus Disease , Mosaic Virus Disease and Healthy Leaf. In this Project classification done using convolutional neural network CNN. In this project textural information is obtained from Gray-level occurrence matrix (glcm) feature extraction. This project is developed in matlab with accuracy of upto 98 percent.

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Prof. Roshan P. Helonde

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Audio Watermarking Using Matlab Project With Source Code | Audio Watermarking For Hiding Text In Audio | Final Year Project

ABSTRACT

           Currently over the millions of digital audio files such as digital songs are copied illegally during file-sharing over the networks. It has resulted as the loss of revenue for music and broadcasting industries. The traditional protection schemes are no longer useful to protect copyright and ownership of multimedia objects. These challenges have prompted significant research in digital audio watermarking for protection and authentication. Watermarking is a technique, which is used in protecting digital information like text, images, videos and audio as it provides copyrights and ownership. The identity of the owner of the audio file can be hidden in the audio file which is called Watermark. Therefore, digital audio watermarking is the process of hiding some information into the audio file in such a way that the quality and the audibility of the audio is not affected. It helps to prevent forgery and impersonation of audio signal. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field. The proposed method involves Embedding and extraction of audio signal using Least Significant Bit. The audio signal which is in .wav  format undergoes segmentation, transformation and embedding the watermarked data and at the last inverse transformation will be carried out. We attempt to develop an efficient method for hiding the information in the audio file such that the copyright information will be protected from illegal copying of the information. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Bone Fracture Detection Using Deep Learning | Bone Fracture Classification Using Matlab Project With Source Code | Final Year Project Code

ABSTRACT

         Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatize this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones. This project is developed using deep learning in matlab.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Traffic Sign Detection Using Image Processing | Traffic Sign Recognition Using Matlab Project With Source Code | Final Year Project Code

ABSTRACT

               Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly use convolutional neural networks in image processing to execute feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in convolutional neural networks in image processing form a classical classifier, which is trained by conventional gradient descent-based implementations, the generalization ability is limited. The performance could be further improved if other favorable classifiers are used in matlab. The main objective of this project is to develop an algorithm so that we can automatically recognize traffic signs. This work uses basic image processing technique for automatically recognizing.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Breast Cancer Classification Using CNN Convolutional Neural Network | Breast Cancer Detection Using Matlab Project With Source Code

ABSTRACT

        The World Health Organization's International agency for Research on Cancer in Lyon, France, estimates that more than 150000 women worldwide die of breast cancer each year. Organ chlorines are considered a possible cause for hormone-dependent cancers . Detection of early and subtle signs of breast cancer requires high-quality images and skilled mammographic interpretation. In order to detect early onset of cancers in breast screening, it is essential to have high-quality images. Radiologists reading mammograms should be trained in the recognition of the signs of early onset of, which may be subtle and may not show typical malignant features. Mammography screening programs have shown to be effective in decreasing breast cancer mortality through the detection and treatment of early onset of breast cancers. In this project we have used image processing for detection of breast cancer like Benign Cancer, Malignant Cancer and Normal Breast with accuracy of up to 98 percent. This project is developed using convolutional neural network in matlab.

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Prof. Roshan P. Helonde 

Mobile: +91-7276355704 

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Email: roshanphelonde@rediffmail.com

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Image Compression Using DWT And DCT Algorithm | DWT And DCT Based Image Compression Using Matlab Project With Source Code

ABSTRACT

            The lossless compression is that allows the original data to be perfectly reconstructed from the compressed data. Lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that probable. The main objective of image compression is to decrease the redundancy of the image data which helps in increasing the capacity of storage and efficient transmission. Image compression aids in decreasing the size in bytes of a digital image without degrading the quality of the image to an undesirable level. Image compression plays an important role in computer storage and transmission. The purpose of data compression is that we can reduce the size of data to save storage and reduce time for transmission. Image compression is a result of applying data compression to the digital image. This project is developed in matlab using dwt and dct algorithm.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

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Mango Fruit Diseases Detection Using Image Processing | Mango Fruit Diseases Detection Using Python Project | Final Year Project Code

ABSTRACT

         The diseases are a major problem faced by all the farmers including fruit farmers. It is a threat for large farmlands because these diseases spread throughout the land and make the fruits inedible, which at the end impact badly on the farmer’s income. Hence early disease detection is very important for the farmers to prevent or to control the propagation of the diseases. The traditional method of fruit disease detection and identification is naked eye observation. Even if this method is sufficient for a home gardener, it is a very inefficient one that requires experience and expertise. As a solution for this problem several computerized approaches are being developed using Image Processing techniques in the resent researches. In our proposed work, we considered Mango fruit, as it is a very popular fruit cultivation in World. In this study we have implemented a computerized model for mango disease identification using Image Processing. This intelligent system can easily detect the diseases and we are getting high accuracy up to 99 % to predict the mango diseases. This project is developed in python.

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Prof. Roshan P. Helonde

Mobile: +917276355704

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Email: roshanphelonde@rediffmail.com

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Pomegranate Disease Detection Using CNN Convolutional Neural Network | Pomegranate Disease Detection Using Python Project With Source Code

ABSTRACT

            Diseases in pomegranate fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this project, a solution for the detection and classification of pomegranate fruit diseases is proposed and experimentally validated. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of pomegranate fruit diseases using convolutional neural network. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of disease in fruits, high variance of defect types. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed. This project is developed using convolutional neural network in python.

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Bone Fracture Detection Using Deep Learning | Bone Fracture Classification Using Python Project With Source Code | Final Year Project Code

ABSTRACT

         Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatize this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones. This project is developed using deep learning in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Plant Species Detection Using Image Processing | Plant Species Classification Using Matlab Project With Source Code | Final Year Project

ABSTRACT

            Image processing is the recent growing technique in the world. It refers to the processing of digital images by means of a digital computer. Images play a major role in human perception. Image analysis is between image processing and computer vision. There are no clear boundaries for in continuum with image processing and computer vision. The useful paradigms for computerized process in determining the image is classified in to three types are low-level process: involve primitive operation such as image pre processing to reduce noise, image enhancement and image sharpening, mid-level: image segmentation and high-level: making sense of image recognized. Here image processing technique is used for medicinal purpose by extracting the features of herbal leaf and authenticating it medicinal qualities. Leaves play the major role for the classification of plants. The sample leaves are taken from various places, plants and shape. The image is captured and further work is carried out. Comparison of test sample image with reference not only requires an experienced but is subjective and prone to human errors. By applying advanced technique of image processing and utilizing the capabilities of the recent advanced computing and data/image storage facilities. The aim of the project is to classify and authenticate the plant materials and herbs widely used for Indian herbal medicinal preparation. The quality and authenticity of these leaves are to be ensured for the preparation of herbal medicines. The plant leaves are thoroughly screened, analyzed and compared with the database to give the correct measures of the texture to which category the leaf belongs to. This project is developed in matlab using image processing.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Brain Tumor Detection Using Convolutional Neural Network CNN | Brain Tumor Detection Using Python Project With Source Code | IEEE Based Project

ABSTRACT

           Brain tumors are the most common issue in children. Brain tumors, either malignant or benign, that originate in the cells of the brain. The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. Brain tumor identification is really challenging task in early stages of life. But now it became advanced with various machine learning algorithms. Now a day’s issue of brain tumor automatic identification is of great interest. In Order to detect the brain tumor of a patient we consider the data of patients like MRI images of a patient’s brain. Here our problem is to identify whether tumor is present in patients brain or not. It is very important to detect the tumors at starting level for a healthy life of a patient. There are many literatures on detecting these kinds of brain tumors and improving the detection accuracies. In this work we used brain tumor detection using convolutional neural network. This project is developed in python. 

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Fingernail Disease Detection Using Deep Learning CNN | Fingernail Disease Classification Using Matlab Project Source Code | Final Year Project Code

ABSTRACT

          This project gives idea to predict diseases using the color of the nail at early stage of diagnosis. The main aim of our project is to analyze the disease without causing harm to humans. In earlier traditional system of disease detection, doctors observe the nails of patients and will predict the disease. Many diseases can be identified by analyzing nails of patients. But it is difficult for human eyes to differentiate the slight changes in color. So it is less accurate and time consuming. Our proposed system can be quite useful to overcome this issue since it is fully computer based. The input to the proposed system is image of nail. The system will process the nail image and will extract the nail’s features to diagnose the disease. Human nail consist of various features, our proposed system uses nail color changes to diagnose the disease. Here, first training set data is prepared from nail images of patients with specific diseases. This training data set is compared with extracted feature from input nail image to obtain the result. In our experiment, we found that training set data are correctly matched with color feature of nail image results. It is focused on the system of image recognition on the basis of color analysis. These selected pixels are processed for further analysis using median filters. The system is fully computer based, so even small discontinuities in color values are observed, and we can detect color changes in the initial stage of disease. This project is developed in matlab using deep learning cnn.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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