2019/01/29 by GuruRaj Awate, Awate, GuruRaj
Computer Science · Neuroscience · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning in Healthcare #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1901.10231
openalex publication_date 2019/01/29 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
There is a need for automatic diagnosis of certain diseases from medical\nimages that could help medical practitioners for further assessment towards\ntreating the illness. Alzheimers disease is a good example of a disease that is\noften misdiagnosed. Alzheimers disease (Hear after referred to as AD), is\ncaused by atrophy of certain brain regions and by brain cell death and is the\nleading cause of dementia and memory loss [1]. MRI scans reveal this\ninformation but atrophied regions are different for different individuals which\nmakes the diagnosis a bit more trickier and often gets misdiagnosed [1, 13]. We\nbelieve that our approach to this particular problem would improve the\nassessment quality by pre-flagging the images which are more likely to have AD.\nWe propose two solutions to this; one with transfer learning [9] and other by\nBellCNN [14], a custom made Convolutional Neural Network (Hear after referred\nto as CNN). Advantages and disadvantages of each approach will also be\ndiscussed in their respective sections. The dataset used for this project is\nprovided by Open Access Series of Imaging Studies (Hear after referred to as\nOASIS) [2, 3, 4], which contains over 400 subjects, 100 of whom have mild to\nsevere dementia. The dataset has labeled these subjects by two standards of\ndiagnosis; MiniMental State Examination (Hear after referred to as MMSE) and\nClinical Dementia Rating (Hear after referred to as CDR). These are some of the\ngeneral tools and concepts which are prerequisites to our solution; CNN [5, 6],\nNeural Networks [10] (Hear after referred to as NN), Anaconda bundle for\npython, Regression, Tensorflow [7]. Keywords: Alzheimers Disease, Convolutional\nNeural Network, BellCNN, Image Recognition, Machine Learning, MRI, OASIS,\nTensorflow\n