Abstract
It is crucial to analyze colorectal cancer histological pictures with objectivity. One of the main causes of mortality globally is colorectal cancer (CRC), sometimes referred to as bowel cancer. Early diagnosis has become crucial for a treatment to be effective. A cutting-edge deep convolutional neural network (CNN) with transfer learning can classify several images of CRC into a variety of categories of artificial intelligence (AI). We have adjusted the Inception V4 model to classify the CRC histopathology images in this study's experiment. We also use transfer learning (TL) and modification techniques for increasing accuracy. Inception V4 net is currently one of the finest CNN designs for identifying CRC histopathology pictures from the National Center for Tumor Diseases (NCT) Biobank an open-source collection, according to the findings of our experiment. Additionally, we are able to achieve 97.7% accuracy using TL on the validation dataset, outperforming all prior values we could find in the literature.
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Patnaik, R., Rath, P.S., Padhy, S., Dash, S. (2023). Histopathological Colorectal Cancer Image Classification by Using Inception V4 CNN Model. In: Sharma, S., Subudhi, B., Sahu, U.K. (eds) Intelligent Control, Robotics, and Industrial Automation. RCAAI 2022. Lecture Notes in Electrical Engineering, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-99-4634-1_79
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