Globally, breast cancer is considered a major reason for women’s morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.

在全球范围内，乳腺癌被认为是影响女性道德的主要原因。早期和准确地识别乳腺癌对于提高生存率至关重要。因此，开发了计算机辅助诊断 (CAD) 模型来帮助放射科医师检测乳房 X 光检查病变。目前，机器学习（ML）和深度学习（DL）模型广泛应用于疾病诊断过程。鉴于此，本文设计了一种新型 CAD，该 CAD 使用带有 MobileNet (CAD-ORGSMN) 模型的最佳区域生长分割，用于乳腺癌识别和分类。所提出的 CAD-ORGSMN 模型涉及不同的操作阶段，即预处理、分割、特征提取和分类。首先，所提出的模型使用基于 Weiner 滤波 (WF) 的预处理技术来消除乳房 X 线照片图像中存在的噪声。CAD-ORGSMN 模型涉及基于萤火虫群优化 (GSO) 的图像分割区域生长技术，其中初始种子点和阈值由 GSO 算法优化创建。此外，使用基于 MobileNet 的特征提取器，其中使用燕群优化 (SSO) 算法优化选择 MobileNet 模型的超参数。最后，变分自动编码器用作分类器来确定输入乳房 X 线照片图像的类别标签。利用 GS​​O 算法进行区域生长技术和 SSO 算法进行超参数优化有助于显着提高 CAD-ORGSMN 模型的乳腺癌检测性能。CAD-ORGSMN 模型的性能验证是针对 Mini-MIAS 数据库进行的，所获得的结果突出了 CAD-ORGSMN 模型在不同测量方面相对于最近最先进的方法的有希望的性能。