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There is a need for automatic diagnosis of certain diseases from medicalimages that could help medical practitioners for further assessment towardstreating the illness. Alzheimers disease is a good example of a disease that isoften misdiagnosed. Alzheimers disease (Hear after referred to as AD), iscaused by atrophy of certain brain regions and by brain cell death and is theleading cause of dementia and memory loss [1]. MRI scans reveal thisinformation but atrophied regions are different for different individuals whichmakes the diagnosis a bit more trickier and often gets misdiagnosed [1, 13]. Webelieve that our approach to this particular problem would improve theassessment 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 byBellCNN [14], a custom made Convolutional Neural Network (Hear after referredto as CNN). Advantages and disadvantages of each approach will also bediscussed in their respective sections. The dataset used for this project isprovided by Open Access Series of Imaging Studies (Hear after referred to asOASIS) [2, 3, 4], which contains over 400 subjects, 100 of whom have mild tosevere dementia. The dataset has labeled these subjects by two standards ofdiagnosis; MiniMental State Examination (Hear after referred to as MMSE) andClinical Dementia Rating (Hear after referred to as CDR). These are some of thegeneral tools and concepts which are prerequisites to our solution; CNN [5, 6],Neural Networks [10] (Hear after referred to as NN), Anaconda bundle forpython, Regression, Tensorflow [7]. Keywords: Alzheimers Disease, ConvolutionalNeural Network, BellCNN, Image Recognition, Machine Learning, MRI, OASIS,Tensorflow

Detection of Alzheimers Disease from MRI using Convolutional Neural Networks, Exploring Transfer Learning And BellCNN