Attention-Driven Lightweight Network For Colorectal Cancer Classification
Keywords:
Colorectal Cancer, Histopathology Images, Deep Learning, MobileNetV2, Attention Mechanism, Xception, Image Classification, Automated Diagnosis, Cancer Detection, Lightweight Neural NetworkAbstract
Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide, making early and accurate diagnosis essential for improving patient outcomes. Although histopathological analysis remains the standard method for CRC diagnosis, manual examination of tissue images can be time-consuming, labor-intensive, and susceptible to variations in human interpretation. To address these limitations, this study presents an efficient attention-based deep learning framework built on the MobileNetV2 architecture for the automated classification of colorectal cancer histopathology images. The proposed model combines the lightweight nature of MobileNetV2 with attention refinement mechanisms to effectively identify and emphasize diagnostically important regions while learning both local and global discriminative features. A comparative evaluation is also conducted using the Xception model as a baseline architecture. Experimental findings indicate that the proposed attention-enhanced MobileNetV2 model delivers competitive, and in some cases improved, classification performance with lower computational requirements. Its lightweight design makes the framework particularly suitable for efficient and potentially real-time clinical implementation. The proposed approach can support pathologists in analyzing histopathological images, reduce diagnostic workload, and improve the consistency and reliability of colorectal cancer detection.
References
[1]. C. Mattiuzzi, F. Sanchis-Gomar, and G. Lippi, “Concise update on colorectal cancer epidemiology,” Annals of Translational Medicine, vol. 7, no. 21, p. 609, Nov. 2019, doi: 10.21037/atm.2019.07.91.
[2]. Y. Xi and P. Xu, “Global colorectal cancer burden in 2020 and projections to 2040,” Translational Oncology, vol. 14, no. 10, Art. no. 101174, Oct. 2021, doi: 10.1016/j.tranon.2021.101174.
[3]. A. G. Russo, A. Andreano, A. Sartore-Bianchi, G. Mauri, A. Decarli, and S. Siena, “Increased incidence of colon cancer among individuals younger than 50 years: A 17-year analysis from the cancer registry of Milan, Italy,” Cancer Epidemiology, vol. 60, pp. 134–140, Jun. 2019, doi: 10.1016/j.canep.2019.03.015.
[4]. N. Murphy, V. Moreno, D. J. Hughes, L. Vodicka, P. Vodicka, E. K. Aglago, M. J. Gunter, and M. Jenab, “Lifestyle, dietary, and environmental factors in colorectal cancer susceptibility,” Molecular Aspects of Medicine, vol. 69, pp. 2–9, Oct. 2019, doi: 10.1016/j.mam.2019.06.005.
[5]. A. H. Sannidhanam, “Self-Healing Distributed Systems: AI-Driven Failure Prediction and Automated Recovery,” International Journal of Research & Technology, vol. 11, no. 4, pp. 168–174, 2023.
[6]. A. H. Sannidhanam, “Autonomous AI Agents for Cloud Infrastructure Operations,” Journal of Contemporary Science and Technology Management, vol. 1, no. 1, pp. 83–100, 2025.
[7]. A. H. Sannidhanam, “LLM-Driven Workflow Optimization: Intelligent Routing in Asynchronous Distributed Systems,” International Journal of AI and Machine Learning, vol. 1, no. 2, pp. 23–33, 2026.
[8]. A. O. Adigun et al., “Causes and prevention of early-onset colorectal cancer,” Cureus, vol. 15, no. 9, Art. no. e45095, 2023, doi: 10.7759/cureus.45095.
[9]. A. H. Sannidhanam and S. Alladi, “Cloud-Based Healthcare CRM Systems for Improved Patient Engagement,” International Journal of Technology, Management and Humanities, vol. 3, no. 1, pp. 32–44, 2017, doi: 10.21590/ijtmh.3.03.4.
[10]. W. Hamilton and S. E. R. Bailey, “Colorectal cancer in symptomatic patients: Improving the diagnostic pathway,” Best Practice & Research Clinical Gastroenterology, vol. 66, Art. no. 101842, Oct. 2023, doi: 10.1016/j.bpg.2023.101842.
[11]. C. L. Niedzwiedz, L. Knifton, K. A. Robb, S. V. Katikireddi, and D. J. Smith, “Depression and anxiety among people living with and beyond cancer: A growing clinical and research priority,” BMC Cancer, vol. 19, p. 943, 2019, doi: 10.1186/s12885-019-6181-4.
[12]. K. S. Wang et al., “Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence,” BMC Medicine, vol. 19, p. 76, 2021, doi: 10.1186/s12916-021-01942-5.
[13]. M. Rana and M. Bhushan, “Machine learning and deep learning approaches for medical image analysis: From diagnosis to detection,” Multimedia Tools and Applications, vol. 82, no. 17, pp. 26731–26769, Jul. 2023, doi: 10.1007/s11042-022-14305-w.
[14]. “Distributed Data Processing Frameworks for Large-Scale Healthcare Analytics,” International Journal of Advance Industrial Engineering, vol. 7, no. 4, pp. 1–10, 2019, doi: 10.14741/ijaie/v.7.4.01.
[15]. A. H. Sannidhanam, “Scalable Web Services Architecture for Healthcare Data Processing,” International Journal of Recent Advances in Science and Technology, vol. 7, no. 1, pp. 1–14, 2020.
[16]. “Event Streaming Architectures for High-Volume Transaction Processing,” International Journal of Advance Industrial Engineering, vol. 10, no. 1, pp. 1–8, 2022.
[17]. A. H. Sannidhanam, “Guardrails and Safety Mechanisms for LLM-Powered Enterprise Applications,” International Journal of Engineering Science & Humanities, vol. 14, no. 3, pp. 273–285, 2024.
[18]. “Performance Analysis of Wireless Sensor Networks for Smart Monitoring Applications,” International Journal of Humanities and Information Technology, vol. 1, no. 4, pp. 10–29, 2016, doi: 10.21590/ijhit.01.04.04.
[19]. A. Bari, I. Khan, R. Samrin, and A. Khare, “VPC & Public Cloud Optimal Performance in Cloud Environment,” Educational Administration: Theory and Practice, vol. 30, no. 6, Jun. 2024.
[20]. A. Nishat, M. A. Bari, and G. Singh, “Mobile Ad Hoc Network Reactive Routing Protocol to Mitigate Misbehavior Node,” International Journal of Intelligent Systems and Applications in Engineering, Nov. 2023, ISSN: 2147-6799.
[21]. Ijteba Sultana, Dr. Mohd Abdul Bari ,Dr. Sanjay,” Routing Performance Analysis of Infrastructure-less Wireless Networks with Intermediate Bottleneck Nodes”, International Journal of Intelligent Systems and Applications in Engineering, ISSN no: 2147-6799 IJISAE,Vol 12 issue 3, 2024, Nov 2023
[22]. V. K. B. Parasaram, V. T. Bathini, S. K. Nalluri, and A. H. Sannidhanam, “A Sustainable AI-Enabled Predictive Maintenance Framework for Smart Industrial Systems Using Industrial IoT Data,” in Proc. 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS), pp. 440–447, 2026, doi: 10.1109/ICICDS70526.2026.11604878.
[23]. L. Adlung, Y. Cohen, U. Mor, and E. Elinav, “Machine learning in clinical decision making,” Med, vol. 2, no. 6, pp. 642–665, Jun. 2021, doi: 10.1016/j.medj.2021.04.006.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
