Baghdadi Nadiah A, Malki Amer, Magdy Balaha Hossam, AbdulAzeem Yousry, Badawy Mahmoud, Elhosseini Mostafa
College of Nursing, Nursing Management and Education Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia.
PeerJ Comput Sci. 2022 Aug 8;8:e1054. doi: 10.7717/peerj-cs.1054. eCollection 2022.
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
由于其高发病率和广泛传播，乳腺癌是一种特别危险的疾病。早期检测和诊断可以提高乳腺癌的生存几率。对于医学图像分析人员来说，诊断工作艰巨、耗时、常规且重复。医学图像分析可能是检测这种疾病的一种有用方法。最近，人工智能技术已被用于帮助放射科医生更快速、可靠地识别乳腺癌。卷积神经网络等技术是很有前景的医学图像识别和分类工具。本研究提出了一个基于组织学和超声数据的自动且可靠的乳腺癌分类框架。该系统基于卷积神经网络构建，并采用迁移学习技术和元启发式优化。部署蝠鲼觅食优化（MRFO）方法以提高框架的适应性。使用乳腺癌数据集（两类）和乳腺超声数据集（三类），研究了八种现代预训练的卷积神经网络架构以应用迁移学习技术。该框架使用MRFO通过优化其超参数来提高卷积神经网络架构的性能。广泛的实验记录了性能参数，包括准确率、AUC、精确率、F1分数、灵敏度、骰子系数、召回率、交并比和余弦相似度。就准确率而言，所提出的框架在组织病理学数据上得分为97.73%，在超声数据上得分为99.01%。实验结果表明，所提出的框架在文献综述中优于其他现有技术方法。
