Dewangan Kranti Kumar, Dewangan Deepak Kumar, Sahu Satya Prakash, Janghel Rekhram
Department of Information Technology, National Institute of Technology, Raipur, Chhatisgarh 492010 India.
Multimed Tools Appl. 2022;81(10):13935-13960. doi: 10.1007/s11042-022-12385-2. Epub 2022 Feb 25.
Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.
乳腺癌是全球女性主要死因之一。因此，对早期乳腺癌进行识别和分类对于帮助患者采取适当措施至关重要。然而，乳房X光造影图像在检测乳腺癌时灵敏度和效率很低。此外，磁共振成像（MRI）在预测乳腺癌方面比乳房X光造影具有更高的灵敏度。在本研究中，开发了一种具有混合磷虾群-非洲水牛优化（HKH-ABO）机制的新型反向传播增强递归维恩梅德模型（BPBRW），用于使用乳腺MRI图像在早期阶段检测乳腺癌。首先，将MRI乳腺图像输入系统进行训练，并建立一种创新的维恩梅德滤波器对MRI噪声图像内容进行预处理。此外，具有HKH-ABO机制的投影BPBRW将乳腺癌肿瘤分类为良性和恶性。此外，该模型使用Python进行模拟，并与现有工作对当前研究工作的性能进行评估。因此，对比图显示，当前研究模型的准确率提高到99.6%，错误率降低了0.12%。
