Early Stage Leukemia Blood Cancer Detection Using Matlab | Early Stage Leukemia Classification Using Image Processing | Final Year Major Projects

ABSTRACT

             Leukemia Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques is use in this study as promising modalities for detection of Leukemia blood cancer. The accuracy rate of the diagnosis of blood cancer by using image processing will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood Cancer detection and analysis from a given photograph of patient’s cancer affected blood sample. The proposed method is using image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Clustering for final decision of blood cancer based on the number of different cells. This project is developed in matlab.

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Cotton Leaf Disease Detection and Classification Using Deep Learning CNN | Cotton Leaf Disease Detection Using Machine Learning | Final Year Major Matlab Projects

ABSTRACT

               India is the second largest in population and many crops Indian farmers can cultivate and Most of the farmers cultivate cotton in large numbers but the cotton leaf disease is the major problem in the past few decades and that results in a loss of crops, their productivity and money as well. General observation by farmers may be time-consuming, expensive and sometimes inaccurate. The Cotton leaf Disease Detection and identifying the disease at an early stage is a very difficult task for the farmers. If the infection or disease on the crops was not identified by the farmers at the initial level then it will be harmful to the crops as well as for farmers. The main purpose of farming is to yield healthy crops with none disease present. It’s very difficult to visually presume the health of cotton leaf. To beat this problem, a machine learning based approach is proposed which can assess the image of the leaf of the plant and detect the disease and therefore the quality of the cotton using machine learning approach. For availing this user got to upload the image then with the assistance of image processing we can get a digitized colour image of a diseased leaf then we can proceed with applying CNN Convolution Neural Network to predict cotton leaf disease. Every disease on a crop has different features which are extracted at each layer of the convolution network. The goal of this application is to develop a system that recognizes cotton crop diseases. During this user has got to upload a picture on the system, Image processing starts with the digitized color image of the diseased leaf. Finally by applying the CNN disease are often predicted. The detection of plant disease may be a vital factor to stop a significant outbreak. Most plant diseases are caused by fungi, bacteria, and viruses. Traditionally farmer visually checks the disease. This project presents an approach for careful detection of diseases and timely handling to stop the crops from heavy losses. The diseases on cotton are a critical issue that creates the sharp decrease within the production of cotton. This project is developed in matlab.

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Skin Disease Detection Using CNN Convolutional Neural Network | Skin Disease Classification Using CNN | Final Year Major Matlab Project With Source Code

ABSTRACT

         Skin cancer also known as melanoma it is one of the deadliest form of cancer if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this research, different imaging techniques like preprocessing method, segmentation and morphological operations are used to analyze and extract the information of cheek’s discoloration lesion by measuring the area of lesion on skin. This project is developed in matlab using convolutional neural network.

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Image Encryption Decryption Using RSA | Image Encryption Decryption Using Matlab Source Code | Final Year Major Projects

ABSTRACT

            Image security is an utmost concern in the web attacks are become more serious. The Image encryption and decryption has applications in internet communication, military communication, medical imaging, multimedia systems, telemedicine, etc. To make the data secure from various attacks the data must be encrypted before it is transmitting. Absolute protection is a difficult issue to maintain the confidentiality of images through their transmission over open channels such as internet or networks and is a major concern in the media, so image Cryptography becomes an area of attraction and interest of research in the field of information security. The project offer proposed system that provides a special kinds of image Encryption image security, Cryptography using RSA algorithm for encrypted images to extract using RSA algorithm. This approach provides high security and it will be suitable for secured transmission of images over the networks or Internet. This project is developed in matlab using RSA technique.

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Mango Leaf Disease Detection Using Image Processing | Mango Plant Disease Classification Using Matlab | Final Year Major Projects

ABSTRACT

            Mango also called as The King of Fruits is one of the important fruit crops cultivated in different countries around the world. India produces about 40% of the global mango production and ranks first among the worlds mango producing countries. It is estimated that, pests and diseases destroy approximately 30 - 40% of the crop yield. The identification of plant diseases plays a vital role in taking disease control measures in order to improve the quality and quantity of crop yield. Automation of plant diseases is very much beneficial as it reduces the monitoring work in large farms. Leaves being the food source for plants, the early and accurate detection of leaf diseases is important. The mango fruit is popular because of its wide range of adaptability, high nutritional value, different variety, delicious taste and excellent flavor. The fruit contains vitamin A and vitamin C in a rich extent. The crop is prone to diseases like Powdery mildew, Anthracnose, Red Rust, etc. Disorders may also impact the plant in the absence of effective case and control measures. The farmer must consult and take professional support for the prevention / control of diseases and crop disorder. New techniques of detecting mango disease are required to promote better control to avoid this crisis. By considering this, project describes image recognition which provides cost effective and scalable disease detection technology. This project further describes new convolutional neural network models which give an opportunity for easy deployment of this technology. This project is developed in matlab using cnn in image processing.

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Oral Cancer Detection Using Image Processing | Oral Cancer Detection Using Matlab Project With Source Code | Final Year Major Project

 ABSTRACT

          Oral cancer  has  a high  incidence and fatality rate,  making it a leading cancer killer. Death rates from oral cancer have remained high over the previous few decades despite progress in oncology therapy. Most people diagnosed with oral cancer will not receive adequate care on time.  Notably, they were leading to low survival rates in the countryside. There has yet to be a comprehensive investigation on enhancing the diagnostic accuracy of oral disease using  handheld  smartphone  photographic  photos.  To  overcome  the  difficulties  associated  with  the automatic detection of  oral illnesses,  we describe an  approach based on smartphone  image diagnosis powered by a deep learning algorithm. The centered rule method of image capture was offered as a quick and easy way to get high-quality pictures of the mouth. A resampling method was proposed to mitigate the influence of image variability from handheld smartphone cameras, and a medium-sized oral dataset with five types  of disorders  was developed based on  this approach.  Finally, we introduce a  recently developed  deep-learning cnn network  to assess oral  cancer  diagnosis. This project is developed in matlab.

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Paddy Leaf Disease Detection Using CNN | Paddy Leaf Disease Classification Using Python Project With Source Code | Final Year Major Project 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 Classification done using Convolutional Neural Network (CNN) classifiers. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging. This project is developed in python.

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Fruit Recognition Using Image Processing | Fruit Identification Using Matlab Project With Source Code | Final Year Major Project Code

ABSTRACT

          The ability to identify the fruits based on the quality in food industry is very important nowadays where every person has become health conscious. There are different types of fruits available in the market. However, to identify best quality fruits is cumbersome task. Therefore, we come up with the system where fruit is detected under natural lighting conditions. The method used is texture detection method and shape detection. For this methodology, we use image processing to detect particular eight type of fruit. This fruit detection project is implemented in matlab using image processing.

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Diabetic Retinopathy Detection Using Deep Learning | Diabetic Retinopathy Classification Using Matlab Project With Source Code | Final Year Project

ABSTRACT

           Diabetic Retinopathy (DR) is a chronic health disease which requires early detection and treatment. It is important to identify DR using an intelligent system for faster prediction since manual examination and detection of the disease are unreliable and highly prone to error. Therefore, various researchers and medical experts have adopted and approached for advanced feature extraction and image classification, for early DR detection. Diabetic Retinopathy is a consequence of diabetes that affects the eyes. Damaged blood vessels in the retina, a light-sensitive tissue, are the primary cause of DR. Patients with Type 1 or Type 2 diabetes are more likely to have this condition. If the patient has a long-term case  of diabetes and  the blood sugar  level is  not regulated consistently, the odds of this  issue developing in the eye increase.  Diabetic  Retinopathy is  one  of  the most  common causes  of  blindness  in  the Western  countries. Preventing Diabetic Retinopathy has  found to be quite beneficial when people with  diabetes are  monitored regularly. This  process is  shown to be essential if Diabetic Retinopathy is discovered in its early stages due to the availability of treatment. Diabetic Retinopathy, the main cause of blindness among working-age adults, affects millions of individuals. Diabetic  Retinopathy  is  a  medical  disorder  in  which  diabetes  mellitus  causes  damage  to  the  retina.  Diabetic Retinopathy  is diagnosed  using  colored  fundus  images,  which  requires  trained clinicians  to recognize  the  presence  and importance  of  several tiny  characteristics,  making  it a  time-consuming  task.  We present  a  deep learning based  technique to  detect Diabetic Retinopathy in fundus images in this project. This project is developed in matlab.

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Grape Leaf Disease Detection And Pesticides Suggestion Using Image Processing | Grape Plant Disease Classification Using Python Project with Source Code | Final Year Major Project

ABSTRACT

         Grapes is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used a image processing model. Image of the leaf is captured using the built-in camera module of a mobile phone. The accuracy achieved is 98 % in this project. This project is developed in python.

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Types of Brain Tumor Detection Using Deep Learning | Brain Tumor Types Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

            A tumor can be defined as a mass which grows without any control of normal forces. Real time diagnosis of tumors by using more reliable algorithms has been an active of the latest developments in medical imaging and detection of brain tumor in MR scan images. Hence image segmentation is the fundamental problem used in tumor detection. Image segmentation can be defined as the partition or segmentation of a digital image into similar regions with a main aim to simplify the image under consideration into something that is more meaningful and easier to analyze visually. Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Image (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During the past few years, brain tumor segmentation in Magnetic Resonance Imaging(MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. Image processing is an active research area in which medical image processing is a highly challenging field. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. This project is developed in python using deep learning.

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Anemia Disease Detection Using Image Processing | Anemia Disease Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

          Anemia is a blood disorder which results from the abnormalities of red blood cells and shortens the life expectancy to 42 and 48 years for males and females respectively. It also causes pain jaundice, shortness of breath, etc. Anemia is characterized by the presence of abnormal cells like sickle cell, ovalocyte, anisopoikilocyte. Sickle cell disease usually presenting in childhood, occurs more commonly in people from parts of tropical and subtropical regions where malaria is or was very common. A healthy RBC is usually round in shape. But sometimes it changes its shape to form a sickle cell structure; this is called as sickling of RBC. Majority of the sickle cells (whose shape is like crescent moon) found are due to low haemoglobin content. An image processing algorithm to automate the diagnosis of sickle-cells present in thin blood smears is developed. Images are acquired using a charge-coupled device camera connected to a light microscope. Clustering based segmentation techniques are used to identify erythrocytes (red blood cells) and Sickle-cells present on microscopic slides. Image features based on colour, texture and the geometry of the cells are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. The proposed image processing based identification of sickle-cells in anemic patient will be very helpful for automatic, sleek and effective diagnosis of the disease.

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Tomato Leaf Disease Detection Using CNN Convolutional Neural Network | Tomato Plant Disease Classification Using Matlab Project with Source Code | Final Year Projects

ABSTRACT

          India is an agricultural country and soybean 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. This project is developed in matlab.

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Rice Leaf Disease Detection Using CNN Convolutional Neural Network | Rice Plant Disease Classification Using Matlab Project with Source Code | Final Year Major Projects Code

ABSTRACT

                Agriculture plays an important role in the economic growth of every country and so it is necessary to ensure its development. The spread of various diseases in rice plants has increased in recent years. There is a variety of plant pathogens such as viral, bacterial, fungal and these can damage different plant parts above and below the ground. Agriculture is the primary source of livelihood for about more than 50% of the Indian population and rice is one of the major food grains of India. It is observed that rice plant diseases are the major contributors to reduce the production & quality of food. Identification of such diseases may improve the production quality. This project gives an idea about deep learning algorithm which is used to detect deadly diseases in rice plants. Much research has been done to automate the rice plant disease detection process using images of the leaf. This project is developed in matlab using Convolutional Neural Network .

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Acne Disease Detection Using Convolutional Neural Network CNN In Image Processing | Acne Disease Classification Using Python Project With Source Code | Final Year Major Projects

ABSTRACT

          Acne is a chronic skin disease occurring from inflammation of pilosebaceous units which are hair follicles under skin and their surrounding sebaceous gland (fatty gland) clog up. Currently, dermatologist has to manually mark a location of acnes on the sheet, then count to quantify and measure treatment progress. This is an unreliable and inaccurate method. Moreover, this method requires dermatologist’s excessive effort. In this project, a novel automatic acne disease detection using Image processing technique is proposed. Acne causes significant physical and psychological problems for patients such as permanent scarring, depression and anxiety from poor self-image. When you have acnes, go to see dermatologist early is the safest way to heal and prevent future permanent scars. Acne can be caused by many factors such as overactive oil glands that produce too much oil, combine with skin cells to make pores in the skin, become plugged and p-acne bacteria cause acne disease. This project is developed in python.

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Real Time Face Recognition Using Image Processing | Real Time Face Recognition Using Matlab Project With Source Code | Final Year Major Projects

ABSTRACT

             The subject of face recognition is as old as computer vision because of the practical importance of the topic and theoretical interest from cognitive scientists. Despite the fact that other methods of identification (such as fingerprints, or iris scans) can be more accurate, face recognition has always remains a major focus of research because of its noninvasive nature and because it is people's primary method of person identification. This electronic document is about face detection. In computer literature face detection has been one of the most studied topics. Given an arbitrary image, the goal of this project is to determine real time face recognition. While this appears to be a trivial task for human beings, it is very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reports reported approaches for face detection, if one were asked to name a single face detection algorithm that has most impact in recent decades, it will most likely be the face detection, which is capable of processing images extremely rapidly and achieve high detection rates.

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Rice Leaf Disease Detection Using Deep Learning | Rice Plant Disease Classification Using Python Project with Source Code | Final Year Projects Code

ABSTRACT

                Agriculture plays an important role in the economic growth of every country and so it is necessary to ensure its development. The spread of various diseases in rice plants has increased in recent years. There is a variety of plant pathogens such as viral, bacterial, fungal and these can damage different plant parts above and below the ground. Agriculture is the primary source of livelihood for about more than 50% of the Indian population and rice is one of the major food grains of India. It is observed that rice plant diseases are the major contributors to reduce the production & quality of food. Identification of such diseases may improve the production quality. This project gives an idea about deep learning algorithm which is used to detect deadly diseases in rice plants. Much research has been done to automate the rice plant disease detection process using images of the leaf. This project is developed in python using deep learning with accuracy of up to 99%.

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Iris Recognition Using Image Processing | Iris Recognition Using Matlab Project Project With Source Code | IEEE Based Final Year Major Projects Codes

ABSTRACT

             This project presents an iris coding method for effective recognition of an individual. The recognition is performed based on a mathematical and computational method. It consists of calculating the differences coefficients of overlapped angular patches from the normalized iris image for the purpose of feature extraction. Iris recognition belongs to the biometric identification. Biometric identification is a technology that is used for the identification an individual based on ones physiological or behavioral characteristics. Iris is the strongest physiological feature for the recognition process because it offers most accurate and reliable results. Iris recognition process mainly involves three stages namely, iris image preprocessing, feature extraction and template matching. In the pre-processing step, iris localization algorithm is used to locate the inner and outer boundaries of the iris. Detected iris region is then normalized to a fixed size rectangular block. In the feature extraction step, texture analysis method is used to extract significant features from the normalized iris image. This project is developed in matlab.

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Wheat Leaf Disease Classification Using CNN Convolutional Neural Network | Wheat Leaf Disease Detection Using Python Project With Source Code

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 using cnn convolutional neural network in python.

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Malaria Detection Using Deep Learning CNN | Malaria Parasite Classification Using Python Project | Final Year Major Projects

ABSTRACT

             Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable.  Malaria is a serious disease which is caused by the parasite of the genus plasmodium. It poses a global problem and warrants an automatic evaluation process because conventional microscopy which is considered the gold standard has proven to be inefficient and its results are hard to store and reproduce. In conventional microscopy the blood of a malaria infected patient is placed in a slide and is observed under a microscope. This is a time consuming and tiring process even with the involvement of an expert technician. In this study we propose a computerized diagnosis which will help in immediate detection of the disease so that proper treatment can be provided to the malaria patient. We propose the usage of image processing techniques to automate the process of parasite detection in blood samples of patients. The proposed system is robust and it is unaffected by exceptional circumstances and achieves high percentages of accuracy. This project is develop in python.

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Image Encryption And Watermarking Using DWT Algorithm | Image Encryption And Watermarking Using Matlab Final Year Project Source Code

ABSTRACT

          The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder’s intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this project encryption and watermarking is proposed using discrete wavelet transform algorithm on both standard and medical images. The project addresses the use of digital rights management in medical field applications such as encrypting and embedding the watermark in medical images. The various quality parameters are used to figure out the evaluation of the developed method. This project is developed in matlab.

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Video Steganography Using Matlab Project With Source Code | Secret Key Based Video Steganography Using Matlab IEEE Based Major Project Code

ABSTRACT

            Information security has become the area of concern as a result of widespread use of communication medium over the internet. This project focuses on the data security approach when combined with encryption and steganography techniques for secret communication by hiding it inside the multimedia files. The high results are achieved by providing the security to data before transmitting it over the internet. The files such as images, audio, video contains collection of bits that can be further translated into images, audio and video. The files composed of insignificant bits or unused areas which can be used for overwriting of other data. This Project explains the proposed algorithm using video steganography for enhancing data security. The Steganography, Cryptography and Digital Watermarking techniques can be used to obtain security and privacy of data. The steganography is the art of hiding data inside another data such as cover medium by applying different steganography techniques. While cryptography results in making the data human unreadable form called as cipher thus cryptography is scrambling of messages. Whereas the steganography results in exploitation of human awareness so it remains unobserved and undetected or intact. It is possible to use all file medium, digital data, or files as a cover medium in steganography. Generally steganography technique is applied where the cryptography is ineffective.

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Banana Fruit Grade Classification Using CNN Convolutional Neural Network | Fruit Grade Detection Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

         Many technological advancements have been developed for precise learning in every field. It is very important to analyze the data in order to extract some useful information. To standardize the quality of bananas it is essential to determine grade of bananas. This project present a Convolutional Neural Network architecture to classify the grade of banana fruits correctly. It learns a set of image features based on a data-driven mechanism and offers a deep indicator of banana’s grade. The computer vision techniques can potentially provide an automated and non-destructive tool for the classification of grading banana. In the field of artificial intelligence, recent advances in deep learning have led to breakthroughs in long-standing tasks such as vision-related problems of feature extraction, image segmentation, and image classification. Among all these techniques, convolutional neural network (CNN) is one of the most successful methods and has acquired a broad application in image classification. The process of transforming photos into the appropriate digital image data in order to extract particular information is known as image processing. Image processing refers to a strategy or approach for processing photos or images by modifying the chosen image data to get accurate information. Processing a picture is made simple by image processing. Utilizing tried-and-true image processing technologies helps increase fruit grading system. This project is developed in matlab using convolutional neural network in image processing.

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Papaya Fruit Disease Detection Using Image Processing | Papaya Fruit Disease Classification Using Python IEEE Project Based Final Year Major Projects

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 Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using image processing. Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. This intelligent system can easily detect the diseases and we are getting high accuracy up to 99 % to predict the papaya diseases.

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Yoga Pose Classification Using Image Processing | Yoga Pose Detection Using Deep Learning Python Project With Source Code | Final Year Major Projects

ABSTRACT

            Yoga is an ancient art with a long history associated with India. It helps in making a person physically fit and provides mental peace at the same time. With the introduction of Covid-19, it is difficult to perform yoga in classes and if performed without guidance it may cause some serious injuries. Here we develop a system that identifies different yoga poses performed by users. This project is developed in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Yoga Pose Classification Using Deep Learning | Yoga Pose Detection Using Python Project With Source Code | Final Year Major Projects Codes

ABSTRACT

            Yoga is an ancient art with a long history associated with India. It helps in making a person physically fit and provides mental peace at the same time. With the introduction of Covid-19, it is difficult to perform yoga in classes and if performed without guidance it may cause some serious injuries. Here we develop a system that identifies different yoga poses performed by users. This project is developed in python using deep learning technique.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Image Watermarking Using DWT and DCT Matlab Project With Source Code | DWT DCT Based Image Watermarking Using Matlab Final Year Major Project

ABSTRACT

             The authenticity & copyright protection are two major problems in handling digital multimedia. The Image watermarking is most popular method for copyright protection by discrete Wavelet Transform (DWT) which performs 2 Level Decomposition of original (cover) image and watermark image is embedded in Lowest Level (LL) sub band of cover image. Inverse Discrete Wavelet Transform (IDWT) is used to recover original image from watermarked image. And Discrete Cosine Transform (DCT) which convert image into Blocks of M bits and then reconstruct using IDCT. In this project we have compared watermarking using DWT-DCT methods performance analysis on basis of PSNR, Similarity factor of watermark and recovered watermark. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Detection of Hard Exudates And Soft Exudates In Fundus Images Using Matlab | Hard And Soft Exudates Segmentation Using Matlab Final Year Major Project

ABSTRACT

           Diabetic retinopathy DR is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. Diabetic retinopathy is a complication of diabetes that is caused by damage to the small blood vessels of the retina. However, early detection and prompt treatment could prevent the vision loss caused by DR. Premature detection of DR is a challenging task, because patients with DR will have no symptoms until vision loss develops. Hence, person with diabetes should have a comprehensive retina screening once in every year. The DR starts with a mild non-proliferative diabetic retinopathy, which usually does not affect the vision. When vision is affected, it is the result of leaking of fluid in the blood vessels called exudates. Exudates can be hard exudates yellow spots seen in the retina and soft exudates pale yellow or white areas with ill-defined edges. An intelligent computer-aided DR detection system is developed for detecting the exudates in the fundus images. An automated tool can be used for assisting the ophthalmologists in the detection process, which gives attention to diabetic patients in receiving effective treatment. The screening tool utilizes intelligent computer algorithms to automatically analyze the features of DR and detect early signs of pathology.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Papaya Fruit Diseases Detection Using Convolutional Neural Network CNN | Papaya Fruit Diseases Classification Using Python Project Final Year IEEE Project 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 Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using Convolutional Neural Network (CNN). Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. This intelligent system can easily detect the papaya diseases. This project is developed in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More