Gururaj Awate

GuruRaj Awate

There is a need for automatic diagnosis of certain diseases from medical images that could help medical practitioners for further assessment towards treating the illness. Alzheimers disease is a good example of a disease that is often misdiagnosed. Alzheimers disease (Hear after referred to as AD), is caused by atrophy of certain brain regions and by brain cell death and is the leading cause of dementia and memory loss [1]. MRI scans reveal this information but atrophied regions are different for different individuals which makes the diagnosis a bit more trickier and often gets misdiagnosed [1, 13]. We believe that our approach to this particular problem would improve the assessment quality by pre-flagging the images which are more likely to have AD. We propose two solutions to this; one with transfer learning [9] and other by BellCNN [14], a custom made Convolutional Neural Network (Hear after referred to as CNN). Advantages and disadvantages of each approach will also be discussed in their respective sections. The dataset used for this project is provided by Open Access Series of Imaging Studies (Hear after referred to as OASIS) [2, 3, 4], which contains over 400 subjects, 100 of whom have mild to severe dementia. The dataset has labeled these subjects by two standards of diagnosis; MiniMental State Examination (Hear after referred to as MMSE) and Clinical Dementia Rating (Hear after referred to as CDR). These are some of the general tools and concepts which are prerequisites to our solution; CNN [5, 6], Neural Networks [10] (Hear after referred to as NN), Anaconda bundle for python, Regression, Tensorflow [7]. Keywords: Alzheimers Disease, Convolutional Neural Network, BellCNN, Image Recognition, Machine Learning, MRI, OASIS, Tensorflow

Gururaj Awate, Sunil Bangare, G Pradeepini et al.

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.