Cerebral Image Classification using Convolution Neural Network

Abstract

The objective of this proposed novel method is to provide an economic, time efficient and accurate diagnosis of cerebral diseases like brain tumor, hemorrhage and the presence of dementia and its type say like Alzheimer. From previous data gathered by the statistics and World Health Organization there are about two lakh fifty thousand deaths only in United States of America,  making the fatality rate to 9.5 percentage. The proposed model not only reduces the chances of error caused unknowingly by humans, but also provides a benchmark for doctors and patients which can be openly available to all. The paradigm mentioned in the paper has rendered the method of identifying anomalies within the brain with Artificial Intelligence in a little technologically more sophisticated and computerized way. The scans will be trained using deep learning Convolutional Neural Network layers with activation functions and small kernels in order to scan the minute details and finely graded textures in the scans. By using data generation methods and using flipped scans, not only does the accuracy increases but so the reliability on the model. The web application will ease out the exigent problems present in today’s ever changing world trying to rely on internet based solutions whenever possible. Convolutional Neural Network is the technique used to optimize the system by making its machine know, predict and anticipate.