Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm

Selvakumar Thirumalaisamy; Kamaleshwar Thangavilou; Hariharan Rajadurai; Oumaima Saidani; Nazik Alturki; Sandeep Kumar Mathivanan; Prabhu Jayagopal; Saikat Gochhait

doi:10.3390/diagnostics13182925

Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm

Diagnostics (Basel). 2023 Sep 12;13(18):2925. doi: 10.3390/diagnostics13182925.

Authors

Selvakumar Thirumalaisamy¹, Kamaleshwar Thangavilou², Hariharan Rajadurai³, Oumaima Saidani⁴, Nazik Alturki⁴, Sandeep Kumar Mathivanan⁵, Prabhu Jayagopal⁶, Saikat Gochhait^{7

8}

Affiliations

¹ Department of Artificial intelligence & Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.
² Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.
³ School of Computing Science and Engineering, VIT Bhopal University, Bhopal-Indore Highway Kothrikalan, Sehore 466114, India.
⁴ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁵ School of Computing Science & Engineering, Galgotias University, Greater Noida 203201, India.
⁶ School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India.
⁷ Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune 412115, India.
⁸ Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia.

Abstract

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO-ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO-ResNet101 over current methodologies.

Keywords: Ant Colony Optimization; ResNet101; breast cancer; convolutional neural network; hyperparameters; transfer learning.

Grants and funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R333), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.