Thirumalaisamy Selvakumar, Thangavilou Kamaleshwar, Rajadurai Hariharan, Saidani Oumaima, Alturki Nazik, Mathivanan Sandeep Kumar, Jayagopal Prabhu, Gochhait Saikat
Department of Artificial intelligence & Data Science, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.
Diagnostics (Basel). 2023 Sep 12;13(18):2925. doi: 10.3390/diagnostics13182925.
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.
乳腺癌是女性死亡的第二大主要原因。早期准确检测在降低其死亡率方面起着至关重要的作用。乳腺癌的及时检测和分类能够实现最有效的治疗。与传统方法相比，卷积神经网络（CNN）显著提高了医学成像中肿瘤检测和分类的准确性。本研究提出了一种用于识别乳腺癌的综合分类技术，利用合成的CNN、增强的优化算法和迁移学习。主要目标是协助放射科医生快速识别异常。为了克服固有局限性，我们采用基于对立学习（OBL）的方法对蚁群优化（ACO）技术进行了改进。然后采用增强蚁群优化（EACO）方法来确定CNN架构的最佳超参数值。我们提出的框架将残差网络101（ResNet101）CNN架构与EACO算法相结合，产生了一个名为EACO-ResNet101的新模型。在MIAS和DDSM（CBIS-DDSM）乳腺X线摄影数据集上进行了实验分析。与传统方法相比，我们提出的模型在CBIS-DDSM数据集上实现了令人印象深刻的98.63%的准确率、98.76%的灵敏度和98.89%的特异性。在MIAS数据集上，该模型实现了99.15%的分类准确率、97.86%的灵敏度和98.88%的特异性。这些结果证明了所提出的EACO-ResNet101相对于当前方法的优越性。
