Martina Jaincy D E, Pattabiraman V
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Sci Rep. 2025 Aug 8;15(1):29014. doi: 10.1038/s41598-025-09974-0.
Breast cancer (BC) is a kind of cancer that is created from the cells in breast tissue. This is a primary cancer that occurs in women. Earlier identification of BC is significant in the treatment process. To lessen unwanted biopsies, Magnetic Resonance Imaging (MRI) is utilized for diagnosing BC nowadays. MRI is the most recommended examination to detect and monitor BC and explain lesion areas as it has a better ability for soft tissue imaging. Even though, it is a time-consuming procedure and requires skilled radiologists. Here, Breast Cancer Deep Convolutional Neural Network (BCDCNN) is presented for Breast Cancer Detection (BCD) using MRI images. At first, the input image is taken from the database and subjected to a pre-processing segment. Adaptive Kalman filter (AKF) is utilized to execute the pre-processing phase. Thereafter, cancer area segmentation is conducted on filtered images by Pyramid Scene Parsing Network (PSPNet). To improve segmentation accuracy and adapt to complex tumor boundaries, PSPNet is optimized using the Jellyfish Search Optimizer (JSO). It is a recent nature-inspired metaheuristic that converges to an optimal solution in fewer iterations compared to conventional methods. Then, image augmentation is performed that includes augmentation techniques namely rotation, random erasing and slipping. Afterwards, feature extraction is done and finally, BCD is conducted employing BCDCNN, wherein the loss function is newly designed based on an adaptive error similarity. It improves the overall performance by dynamically emphasizing samples with ambiguous predictions, enabling the model to focus more on diagnostically challenging cases and enhancing its discriminative capability. Furthermore, BCDCNN acquired 90.2% of accuracy, 90.6% of sensitivity and 90.9% of specificity. The proposed method not only demonstrates strong classification performance but also holds promising potential for real-world clinical application in early and accurate breast cancer diagnosis.
乳腺癌（BC）是一种起源于乳腺组织细胞的癌症。这是一种发生在女性身上的原发性癌症。早期发现乳腺癌在治疗过程中至关重要。为了减少不必要的活检，如今磁共振成像（MRI）被用于诊断乳腺癌。MRI是检测和监测乳腺癌并解释病变区域的最推荐检查方法，因为它在软组织成像方面具有更好的能力。尽管如此，它是一个耗时的过程，并且需要技术熟练的放射科医生。在此，提出了用于使用MRI图像进行乳腺癌检测（BCD）的乳腺癌深度卷积神经网络（BCDCNN）。首先，从数据库中获取输入图像并进行预处理阶段。自适应卡尔曼滤波器（AKF）用于执行预处理阶段。此后，通过金字塔场景解析网络（PSPNet）对滤波后的图像进行癌区分割。为了提高分割精度并适应复杂的肿瘤边界，使用水母搜索优化器（JSO）对PSPNet进行优化。它是一种最近受自然启发的元启发式算法，与传统方法相比，能在更少的迭代中收敛到最优解。然后，进行图像增强，包括旋转、随机擦除和滑动等增强技术。之后，进行特征提取，最后，使用BCDCNN进行乳腺癌检测，其中基于自适应误差相似度新设计了损失函数。它通过动态强调预测模糊的样本提高了整体性能，使模型能够更多地关注诊断具有挑战性的病例并增强其判别能力。此外，BCDCNN的准确率为90.2%，灵敏度为90.6%，特异性为90.9%。所提出的方法不仅展示了强大的分类性能，而且在早期准确的乳腺癌诊断的实际临床应用中具有广阔的前景。
