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Convolution Neural Networks for diagnosing colon and lung cancer histopathological images

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Document pages: 10 pages

Abstract: Lung and Colon cancer are one of the leading causes of mortality andmorbidity in adults. Histopathological diagnosis is one of the key componentsto discern cancer type. The aim of the present research is to propose acomputer aided diagnosis system for diagnosing squamous cell carcinomas andadenocarcinomas of lung as well as adenocarcinomas of colon using convolutionalneural networks by evaluating the digital pathology images for these cancers.Hereby, rendering artificial intelligence as useful technology in the nearfuture. A total of 2500 digital images were acquired from LC25000 datasetcontaining 5000 images for each class. A shallow neural network architecturewas used classify the histopathological slides into squamous cell carcinomas,adenocarcinomas and benign for the lung. Similar model was used to classifyadenocarcinomas and benign for colon. The diagnostic accuracy of more than 97 and 96 was recorded for lung and colon respectively.

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