Houssein Essam H, Emam Marwa M, Ali Abdelmgeid A
Faculty of Computers and Information, Minia University, Minia, Egypt.
Neural Comput Appl. 2022;34(20):18015-18033. doi: 10.1007/s00521-022-07445-5. Epub 2022 Jun 8.
Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.
乳腺癌是女性第二大死因；因此，有效早期检测这种癌症可降低其死亡率。在乳腺癌发展的早期阶段进行检测和分类有助于实现最佳治疗。与传统方法相比，卷积神经网络（CNN）提高了医学成像中肿瘤检测和分类的效率。本文提出了一种基于混合CNN和改进优化算法以及迁移学习的新型乳腺癌诊断分类模型，以帮助放射科医生高效检测异常。我们使用的优化算法是海洋捕食者算法（MPA），并使用基于对立学习策略对其进行改进，以应对原始MPA隐含的弱点。改进的海洋捕食者算法（IMPA）用于寻找CNN架构超参数的最佳值。所提出的方法使用了一个名为ResNet50（残差网络）的预训练CNN模型。该模型与IMPA算法相结合，产生了一个名为IMPA-ResNet50的架构。我们在两个乳腺X线摄影数据集上进行评估，即乳腺X线图像分析协会（MIAS）数据集和数字数据库筛查乳腺摄影（DDSM）数据集中精心策划的乳腺成像子集（CBIS-DDSM）。将所提出的模型与其他先进方法进行了比较。所得结果表明，所提出的模型优于所比较的先进方法，这有利于分类性能，在CBIS-DDSM数据集上实现了98.32%的准确率、98.56%的灵敏度和98.68%的特异性，在MIAS数据集上实现了98.88%的准确率、97.61%的灵敏度和98.40%的特异性。为了评估IMPA在寻找ResNet50架构超参数最优值方面的性能，将其与其他四种优化算法进行了比较，包括引力搜索算法（GSA）、哈里斯鹰优化算法（HHO）、鲸鱼优化算法（WOA）和原始MPA算法。相应的算法也与ResNet50架构相结合，分别产生了名为GSA-ResNet50、HHO-ResNet50、WOA-ResNet50和MPA-ResNet50的模型。结果表明，所提出的IMPA-ResNet50比其他对应模型具有更好的性能。
