Ensemble-based Multi-tissue Classification Approach Of Colorectal Cancer Histology Images Using A Novel Hybrid Deep Learning Framework

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Deep learning (DL) methods can provide a viable alternative to histopathology image (HI) analysis, however, DL architectures may not be sufficient to classify CRC tissues based on anatomical histopathology data. To address this issue, a hybrid deep learning method was proposed which combines a dilated ResNet (dResNet) structure and attention module with neighborhood component analysis (NCA) and a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM. The hybrid model achieved 98.75% and 99.76% accuracy on CRC datasets, outperforming state-of-the-art approaches in terms of computational efficiency and time. This hybrid deep learning method can be used to accurately predict CRC based on pathology image classification.