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

Larissa Ferreira Rodrigues Moreira; André Ricardo Backes

doi:10.22456/2175-2745.150622

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

Authors

Larissa Ferreira Rodrigues Moreira Universidade Federal de Viçosa (UFV)
André Ricardo Backes Universidade Federal de São Carlos (UFSCar) https://orcid.org/0000-0002-7486-4253

DOI:

https://doi.org/10.22456/2175-2745.150622

Keywords:

colorectal cancer, histological images, lightweight CNN, texture features, ensemble learning

Abstract

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.

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Published

2026-03-10

How to Cite

Ferreira Rodrigues Moreira, L., & Backes, A. R. (2026). Ensemble of Handcrafted and Learned Features using a Lightweight CNN for Colorectal Cancer Classification. Revista De Informática Teórica E Aplicada, 33(2), 326–332. https://doi.org/10.22456/2175-2745.150622