Detection of Alzheimers Disease from MRI using Convolutional Neural Network with Tensorflow

TL;DR

This paper explores using convolutional neural networks with TensorFlow to automatically diagnose Alzheimer's disease from MRI scans, leveraging high-performance GPU computing for improved accuracy.

Contribution

It introduces a CNN-based approach for Alzheimer's detection from MRI images, utilizing the OASIS dataset and GPU acceleration for enhanced diagnostic performance.

Findings

CNN achieves low error rate in Alzheimer's classification

GPU acceleration significantly reduces training time

Effective feature recognition in MRI images

Abstract

Nowadays, due to tremendous improvements in high performance computing, it has become easier to train Neural Networks. We intend to take advantage of this situation and apply this technology in solving real world problems. There was a need for automatic diagnosis certain diseases from medical images that could help a doctor and radiologist for further action towards treating the illness. We chose Alzheimer disease for this purpose. Alzheimer disease is the leading cause of dementia and memory loss. Alzheimer disease, it is caused by atrophy of the certain brain regions and by brain cell death. MRI scans reveal this information but atrophy regions are different for different people which makes the diagnosis a little trickier and often gets miss-diagnosed by doctors and radiologists. The Dataset used for this project is provided by OASIS, which contains over 400 subjects 100 of which having mild to severe dementia and is supplemented by MMSE and CDR standards of diagnosis in the same context. Enter CNN, Convolutional Neural Networks are a hybrid of Kernel Convolutions and Neural Networks. Kernel Convolutions is a technique that uses filters to recognize and segment images based on features. Neural Networks consist of neurons which are loosely based on human brains neuron which represents a single classifier and interconnected by weights, have different biases and are activated by some activation functions. By using Convolutional Neural Networks, the problem can be solved with minimal error rate. The technologies we intend to use are libraries like CUDA CuDNN for making use of GPU and its multiple cores-parallel computing to train models while giving us high performance.