Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kaatankulathur, Chennai, 603203, India.
Department of Computer Science and Engineering, Periyar Maniammai Institute of Science & Technology, Vallam, Thanjavur, 613403, India.
Sci Rep. 2024 Nov 17;14(1):28376. doi: 10.1038/s41598-024-79363-6.
Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body; this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it is measured by providing patients with the essential treatment so their efficient lifetime can be protected. Scientists and researchers in numerous studies have initiated techniques to identify tumours in early phases. Still, misperception in classifying skeptical lesions can be due to poor image excellence and dissimilar breast density. BC is a primary health concern, requiring constant initial detection and improvement in analysis. BC analysis has made major progress recently with combining multi-modal image modalities. These studies deliver an overview of the segmentation, classification, or grading of numerous cancer types, including BC, by employing conventional machine learning (ML) models over hand-engineered features. Therefore, this study uses multi-modality medical imaging to propose a Computer Vision with Fusion Joint Transfer Learning for Breast Cancer Diagnosis (CVFBJTL-BCD) technique. The presented CVFBJTL-BCD technique utilizes feature fusion and DL models to effectively detect and identify BC diagnoses. The CVFBJTL-BCD technique primarily employs the Gabor filtering (GF) technique for noise removal. Next, the CVFBJTL-BCD technique uses a fusion-based joint transfer learning (TL) process comprising three models, namely DenseNet201, InceptionV3, and MobileNetV2. The stacked autoencoders (SAE) model is implemented to classify BC diagnosis. Finally, the horse herd optimization algorithm (HHOA) model is utilized to select parameters involved in the SAE method optimally. To demonstrate the improved results of the CVFBJTL-BCD methodology, a comprehensive series of experimentations are performed on two benchmark datasets. The comparative analysis of the CVFBJTL-BCD technique portrayed a superior accuracy value of 98.18% and 99.15% over existing methods under Histopathological and Ultrasound datasets.
乳腺癌（BC）是一种从乳腺组织发展和扩散并逐渐超过全身的癌症；这种癌症起源于两性。在这个阶段，及时发现这种疾病是最重要的，通过为患者提供必要的治疗来保护他们的有效寿命来衡量。许多研究的科学家和研究人员已经开始使用技术来识别早期阶段的肿瘤。然而，由于图像质量差和乳房密度不同，对可疑病变的分类可能存在误解。BC 是一个主要的健康关注点，需要不断进行初步检测和分析改进。BC 分析最近在结合多模态图像模式方面取得了重大进展。这些研究通过在手工制作的特征上使用传统的机器学习（ML）模型，提供了乳腺癌等多种癌症类型的分割、分类或分级概述。因此，本研究使用多模态医学成像，提出了一种用于乳腺癌诊断的计算机视觉融合联合转移学习技术（CVFBJTL-BCD）。所提出的 CVFBJTL-BCD 技术利用特征融合和深度学习模型来有效检测和识别 BC 诊断。CVFBJTL-BCD 技术主要采用 Gabor 滤波（GF）技术去除噪声。接下来，CVFBJTL-BCD 技术使用基于融合的联合转移学习（TL）过程，该过程包括三个模型，即 DenseNet201、InceptionV3 和 MobileNetV2。使用堆叠自动编码器（SAE）模型对 BC 诊断进行分类。最后，利用马群优化算法（HHOA）模型对 SAE 方法中涉及的参数进行最优选择。为了证明 CVFBJTL-BCD 方法的改进结果，在两个基准数据集上进行了一系列全面的实验。CVFBJTL-BCD 技术的比较分析在组织病理学和超声数据集上分别表现出 98.18%和 99.15%的卓越准确性值，优于现有方法。
