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.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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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.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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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.

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 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.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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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.

PROJECT OUTPUT

PROECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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