Harnessing Artificial Intelligence for Lung and Colon Cancer Classification via CNN

Abstract

The aggressiveness, strong propensity to spread, and heterogeneity of cancer seem to be the main causes of its very high fatality rate. Throughout the world, lung and colon cancers are two of the most common malignancies that affect individuals of all ages. Accurate and timely detection of these cancers may improve the best aspects of treatment and increase the survival rate. As a complement to the current cancer detection techniques, an extremely exact and computationally effective model is proposed for the rapid and accurate diagnosis of cancers in the lung and colon area. By employing a cyclic learning rate, the accuracy of the proposed techniques is increased while maintaining their processing efficiency. This is easy to use and effective, which speeds up the model's convergence. Furthermore, many transfer learning models that have already been trained are used and compared with the proposed CNN that has attention layers. The validation, testing, and training of the study make use of the LC25000 dataset. It is observed that the proposed model reduces the impact of inter-class disparities between lung adenocarcinoma and lung cancer of squamous cells by offering higher accuracy. By putting the proposed framework into practice, accuracy was improved to 99.04%.