Ensemble of Handcrafted and Learned Features using a Lightweight CNN for Colorectal Cancer Classification

Colorectal cancer (CRC) is one of the highest incident cancers in the world. The late-stage diagnosis plays a pivotal role in the mortality rate, making CRC the second leading cause of cancer-related deaths. Its diagnosis is based on the analysis of histological images acquired from a biopsy, a time-consuming and prone to errors task. Over the years, many deep learning and computer vision approaches have been proposed to automatize such a task, reducing the need for human specialists. To contribute to this area of research, we proposed an ensemble that combines a Lightweight CNN and handcrafted color texture features commonly used in literature. We investigated how different texture methods impact the performance of the ensemble in an important multi-class problem composed of eight types of tissues. Our ensemble obtained 99.63% accuracy, surpassing state of the art methods and deeper CNNs, and 99.63% F1 score, showing a good balance between precision and recall.