Developing A Single Deep Learning Framework Designed To Model And Understand A Spectrum Of Different Diseases

Abstract

Now-a-days several people die due to various diseases. Also, for every disease detection there include multiple technologies and techniques. As there are various Techniques and technologies, we developed a platform combining several diseases detection under one platform. In this we included the diseases Brain Tumor detection, Breast Cancer, Alzheimer, Pneumonia, Heart Disease detection and Diabetes detection. here we create a online web application where we merge all the techniques which are needed for various disease detection. A convolutional neural community is a deep getting to know set of rules which takes picture as an input and assign some weights and bias to various objects in picture and as a end result in a position to differentiate one from some other. Convolutional neural community can be capable of efficiently capture the spatial and temporal dependencies in an image via applicable filters in an utility. The main advantage of this convolutional neural is it process the image into a format where we cannot any features and also it becomes easier to process and we can get the results accurately. The architecture of convolutional networks is similar to human brains regarding the connectivity of neurons. For Brain tumor detection, Pneumonia detection and Alzheimer detection we use convolutional neural network. It is used for feature extraction. For Breast cancer and we use Random Forest algorithm. For heart disease detection we use XGboost which is a popular and efficient open-source implementation of the gradient boosted trees algorithm. The main advantage of this project is we can get the test results immediately with few clicks. By sitting inour home we can able to predict the whether the person is suffering from particular disease or not with a basic detail. This project is mainly focused on patients’ perspective.