乳腺癌是对妇女产生广泛影响且致命的疾病。当乳房细胞生长出恶性肿块时，就会引起乳腺癌。自我分析和定期医学检查有助于早期检测疾病并提高生存率。因此，在乳房X线照片中使用自动化乳腺癌检测系统可以协助临床医生的治疗工作。在医学技术中，乳腺癌的分类对研究人员和调查员来说非常具有挑战性。深度学习方法的进步已经引起了人们对其在医学图像问题上的优势的更多关注，尤其是在乳腺癌检测方面。本研究计划开发一种新型的混合模型用于乳腺癌诊断，并借助优化的深度学习体系结构提供支持。所需图像来自基准数据集。这些收集的数据集经过三种预处理方法处理，包括“中值滤波、直方图均衡化和形态学操作”，以帮助去除图像中的不需要的区域。然后，将预处理过的图像应用于基于优化的U-Net的肿瘤分割阶段，以获得准确的分割结果，并通过应用“适应性黑寡妇优化”来优化U-Net中的某些参数。然后，以两种不同的方法进行检测，即模型1和模型2。在模型1中，使用分割的肿瘤通过“灰度共生矩阵（GLCM）和局部梯度模式（LGP）”提取显著模式，并将这些提取的模式用于执行乳腺癌检测的“双模型访问优化的长短期记忆（DM-OLSTM）”，得到检测得分1。在模型2中，将同样的分割肿瘤输入到不同变体的卷积神经网络（CNN）中，例如“VGG19、Resnet150和Inception”。从这三种基于CNN的方法中提取的深度特征被融合形成一组深度特征。这些融合的深度特征被插入到开发的DM-OLSTM中，以获得用于乳腺癌诊断的检测得分2。在混合模型的最后阶段，从模型1和模型2获得的得分1和得分2取平均得到最终的检测输出。所提供的DM-OLSTM模型的准确度和F1分数分别达到了96%和95%。实验分析证明，通过与基准数据集进行分析，推荐的方法能够实现更好的性能。因此，设计的模型对于实时应用中的乳腺癌检测非常有帮助。版权所有©2023年Elsevier B.V.发表。

Breast cancer is the most fatal disease that widely affects women. When the cancerous lumps grow from the cells of the breast, it causes breast cancer. Self-analysis and regular medical check-ups help for detecting the disease earlier and enhance the survival rate. Hence, an automated breast cancer detection system in mammograms can assist clinicians in the patient's treatment. In medical techniques, the categorization of breast cancer becomes challenging for investigators and researchers. The advancement in deep learning approaches has established more attention to their advantages to medical imaging issues, especially for breast cancer detection.The research work plans to develop a novel hybrid model for breast cancer diagnosis with the support of optimized deep-learning architecture.The required images are gathered from the benchmark datasets. These collected datasets are used in three pre-processing approaches like "Median Filtering, Histogram Equalization, and morphological operation", which helps to remove unwanted regions from the images. Then, the pre-processed images are applied to the Optimized U-net-based tumor segmentation phase for obtaining accurate segmented results along with the optimization of certain parameters in U-Net by employing "Adapted-Black Widow Optimization (A-BWO)". Further, the detection is performed in two different ways that is given as model 1 and model 2. In model 1, the segmented tumors are used to extract the significant patterns with the help of the "Gray-Level Co-occurrence Matrix (GLCM) and Local Gradient pattern (LGP)". Further, these extracted patterns are utilized in the "Dual Model accessed Optimized Long Short-Term Memory (DM-OLSTM)" for performing breast cancer detection and the detected score 1 is obtained. In model 2, the same segmented tumors are given into the different variants of CNN, such as "VGG19, Resnet150, and Inception". The extracted deep features from three CNN-based approaches are fused to form a single set of deep features. These fused deep features are inserted into the developed DM-OLSTM for getting the detected score 2 for breast cancer diagnosis. In the final phase of the hybrid model, the score 1 and score 2 obtained from model 1 and model 2 are averaged to get the final detection output.The accuracy and F1-score of the offered DM-OLSTM model are achieved at 96 % and 95 %.Experimental analysis proves that the recommended methodology achieves better performance by analyzing with the benchmark dataset. Hence, the designed model is helpful for detecting breast cancer in real-time applications.Copyright © 2023. Published by Elsevier B.V.