ATTENTION-BASED NETWORK FOR COLORECTAL CANCER CLASSIFICATION USING MOBILENETV2

Abstract

Colorectal cancer (CRC) is among the leading causes of cancer-related deaths worldwide, emphasizing the importance of accurate and timely diagnosis. Histopathological image analysis remains the gold standard for colorectal cancer diagnosis; however, manual examination is labor-intensive and prone to inter-observer variability. Recent advances in deep learning have significantly improved automated cancer classification by leveraging convolutional neural networks (CNNs). Nevertheless, conventional CNN architectures often fail to capture the most discriminative regions of tissue images, resulting in suboptimal classification performance. This paper proposes an Attention-Based MobileNetV2 (AB-MobileNetV2) framework that integrates a lightweight MobileNetV2 backbone with an attention mechanism to enhance feature representation for colorectal cancer classification. The proposed architecture employs channel and spatial attention modules to emphasize informative pathological regions while suppressing irrelevant background information. Extensive experiments conducted on benchmark colorectal histopathological datasets demonstrate that the proposed model achieves superior classification performance compared to conventional CNN models while maintaining computational efficiency suitable for real-time clinical applications. Experimental results show an overall accuracy of 98.92%, precision of 98.75%, recall of 98.63%, F1-score of 98.69%, and AUC of 99.34%, outperforming several state-of-the-art approaches.