Munteanu, Dan (Dunărea de Jos University of Galați), Moldovanu, Simona (Dunarea de Jos University), Tăbăcaru, Gigi (“Dunarea de Jos” University of Galati), Barbu, Marian (Dunarea de Jos University of Galati)

Influence of Symmetric and Asymmetric CAE-CNN on Colon Cancer Histopathological Images Classification

Scheduled for presentation during the Invited Session "Intelligent Data processing" (WeBB), Wednesday, June 11, 2025, 15:20−15:40, Room A

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

The classification of histopathological images that contain repetitive patterns is a challenge for AI (artificial intelligence) algorithms developed in the last years. Histopathologic diagnosis continues to be the gold standard for cancer diagnosis despite the quick advances in medical research. Therefore, this study proposes to analyse the factors that influence the classification of benign and adenocarcinomas in colon cancer histopathological images, using four different CAE-CNNs (AutoEncoder Convolutional Neural Networks). This paper describes the geometry and tests for each of the following CAE-CNN: (i) symmetric encoder-decoder with bottleneck layers; (ii) asymmetric encoder-decoder with bottleneck layers; (iii) symmetric encoder-decoder without bottleneck layers; (iv) asymmetric encoder-decoder without bottleneck layers. , where only colon histopathological images were classified. The obtained results are very remarkable: an accurate symmetric encoder-decoder with bottleneck layers that achieves 99.2% accuracy on test images.