Colon histology slide classification with deep-learning framework using individual and fused features

Abstract

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (i) Image collection, resizing, and pre-processing; (ii) Deep-Features (DF) extraction with a chosen scheme; (iii) Binary classification with a 5-fold cross-validation; and (iv) Verification of the clinical significance. This work classifies the considered image database using the follwing: (i) Individual DF, (ii) Fused DF, and (iii) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.