Reza, Md TanzimHossain, MainulHaque, Shataddru ShyanAhmed, HumayunMahdi, Hossain AlAich, Ankan2022-05-252022-05-2520222022-01ID 15341003ID 21241079ID 17101358ID 17201084ID 18101445http://hdl.handle.net/10361/16671Cataloged from PDF version of thesis.Includes bibliographical references (pages 32-34).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Colon cancer is one the most prominent and daunting life threatening illnesses in the world. Histopathological diagnosis is one of the most important factors in determining cancer type. The current study aims to create a computer-aided diagnosis system for differentiating tissue cells, benign colon tissues, and adenocarcinomas tissues of the colon, using convolutional neural networks and digital pathology images for such tumors. As a result, in the coming years, artificial intelligence will be a promising technology. The LC25000 dataset, which included 5000 photographs for each class, produced a total of 25000 digital images for lung and colonic cancer cells, as well as healthy cells. The photos of lung cancer were not included in our study because it was primarily focused on colon cancer. To categorize and classify the histopathological slides of adenocarcinomas and benign cells in the colon, a Convolutional neural network architecture was implemented. We also explored optimization techniques such as Explainable AI techniques, Lime and DeepLift to better understand the reasoning behind the decision the model arrived at. This allowed us to better understand and optimize our models for a more consistent accurate classification. Diagnosis validity of greater than 94% was obtained for colon distinguishing adenocarcinoma and benign colonic cells.34 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Colon CancerDeep learningCNNImage classificationWhole slide imagesHistopathological imagesExplainable AIOptimization algorithmsLIMEArtificial intelligenceCognitive learning theory (Deep learning)Thesis