Identification of Colon Cancer Using CNN in Deep Learning

Resumen

Deep learning (DL) has made substantial progress in healthcare during the previous two decades [1]. During this time, researchers discovered the true causes of a number of ailments, established new diagnostic tools, and produced novel remedies. Colon and lung cancer are two of the most serious and fatal diseases that individuals worldwide confront, and they have become a major medical concern. On the other hand, early identification of the disease considerably increases the odds of survival[3].This study employs the EfficientNetB7 Transfer Learning (TL) technique to develop a cataloging model that incorporates histopathology images to distinguish between five types of lung and colon tissues (two benign and three malignant). Furthermore, using histogram photos from the Kaggle collection, a mechanism for predicting lung and colon cancer has been developed [8]. The EfficientNetB7 model obtained a significant accuracy of 98 percent, according to the results. This model will aid medical experts in developing an effective and appropriate approach for detecting various types of lung and colon cancers [5].