A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance

Abstract

Colon cancer remains a leading cause of cancer-related mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods rely heavily on manual interpretation by pathologists, which can result in inaccuracies and delays in treatment. This study proposes an innovative, automated approach to colon cancer diagnosis by integrating advanced machine learning techniques with deep learning architectures. We employed EfficientNet, a state-of-the-art convolutional neural network, to extract intricate features from histopathological images, alongside the Non-dominated Sorting Genetic Algorithm II for optimal feature selection. This hybrid approach significantly enhances diagnostic performance while reducing computational complexity. The model was evaluated using five diverse datasets: Colon Cancer Histopathological Images, Kvasir, Kvasir-SEG, Hyper-Kvasir, and Endotect. The results indicate that our method outperforms traditional models such as CNN, AlexNet, ResNet, and GoogleNet, achieving an accuracy of 99.97% on the Colon Cancer Histopathological Images dataset. These findings suggest that this novel approach can substantially enhance early detection and diagnosis of colon cancer, providing a scalable solution to current diagnostic challenges. Ultimately, our study lays the groundwork for future advancements in automated cancer diagnostics, contributing to improved patient outcomes and more efficient healthcare delivery. The code and dataset for reproducing these results are publicly accessible at https://github.com/Noushin-Saba/ColonCancerDetectionandDiagnosis.