Intelligent Computing Lab, Department of Computer Science and Engineering, National Institute of Technology, Calicut, PO Box: 673601, Kerala, India.
Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai 50603, Kuala Lumpur, Malaysia.
J Healthc Eng. 2022 Feb 28;2022:4295221. doi: 10.1155/2022/4295221. eCollection 2022.
Breast cancer is one of the most common forms of cancer. Its aggressive nature coupled with high mortality rates makes this cancer life-threatening; hence early detection gives the patient a greater chance of survival. Currently, the preferred diagnosis method is mammography. However, mammography is expensive and exposes the patient to radiation. A cost-effective and less invasive method known as thermography is gaining popularity. Bearing this in mind, the work aims to initially create machine learning models based on convolutional neural networks using multiple thermal views of the breast to detect breast cancer using the Visual DMR dataset. The performances of these models are then verified with the clinical data. Findings indicate that the addition of clinical data decisions to the model helped increase its performance. After building and testing two models with different architectures, the model used the same architecture for all three views performed best. It performed with an accuracy of 85.4%, which increased to 93.8% after the clinical data decision was added. After the addition of clinical data decisions, the model was able to classify more patients correctly with a specificity of 96.7% and sensitivity of 88.9% when considering sick patients as the positive class. Currently, thermography is among the lesser-known diagnosis methods with only one public dataset. We hope our work will divert more attention to this area.
乳腺癌是最常见的癌症之一。其侵袭性强，死亡率高，因此对生命构成威胁；因此，早期发现能使患者有更大的生存机会。目前，首选的诊断方法是乳房 X 光摄影术。然而，乳房 X 光摄影术昂贵且使患者暴露于辐射下。一种称为热成像的具有成本效益且侵入性较小的方法正日益流行。考虑到这一点，这项工作旨在最初使用 Visual DMR 数据集，基于卷积神经网络创建使用乳房的多个热视图来检测乳腺癌的机器学习模型。然后使用临床数据验证这些模型的性能。研究结果表明，将临床数据决策添加到模型中有助于提高其性能。在使用不同架构构建和测试了两个模型之后，使用所有三个视图的相同架构的模型表现最佳。它的准确率为 85.4%，在添加临床数据决策后增加到 93.8%。在添加临床数据决策后，该模型能够以 96.7%的特异性和 88.9%的灵敏度正确分类更多的患者，将患病患者视为阳性病例。目前，热成像术是鲜为人知的诊断方法之一，只有一个公共数据集。我们希望我们的工作将引起更多人对这一领域的关注。
