Advanced Automation for Colorectal Tissue Classification in Histopathology

Colorectal cancer is one of the major global health issue due to it high death rate that requires an urgent need for its early detection that can improve patient outcomes. Previous and old methods for diagnosis of colorectal cancer included histopathological inspection, a method that was not only time consuming but highly relies on the medical professions judgment and opinions which would make it a long process. To tackle this problem, our research points out a very different and cutting-edge approach that includes multiple convolutional neural networks to automate the process of classification of histopathological images for multi-class colorectal tissue. An exceptional aspect of our research is the in-depth exploration of the computational time, a critical factor which is often ignored. In our research we found out that our proposed methodology not only improves the classification accuracy but also reduce the computational timing significantly, that makes our research more practical and implementational. Our study not only aims to improve advancement of automated histopathological image classification but also encourages the practical benefits of our model making it a valuable asset in the ongoing efforts to improve early detection and diagnosis of colorectal cancer.