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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Prof. Roshan P. Helonde

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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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