Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models

Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the survival rate. In this study, the performance of a wide variety of deep learning-based architectures is evaluated for automatic tumor segmentation of colorectal tissue samples. The proposed approach highlights the utility of incorporating convolutional neural network modules and transfer learning in the encoder part of a segmentation architecture for histopathology image analysis. A comparative and extensive experiment was conducted on a challenging histopathological segmentation task to demonstrate the effectiveness of incorporating deep modules in the segmentation encoder-decoder network as well as the contributions of its components. Experimental results demonstrate that shared DenseNet and LinkNet architecture is promising, achieves the state-of-the-art performance, and outperforms other methods with a dice similarity index of 82.74%±1.77, accuracy of 87.07%±1.56, and f1-score value of 82.79%±1.79.