  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
