Department of Medical Laboratory Technologies, Al-Maarif University College, Iraq.
College of Technical Engineering, Al-Farahidi University, Baghdad, Iraq.
Biomed Res Int. 2022 Jun 3;2022:2525433. doi: 10.1155/2022/2525433. eCollection 2022.
In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.
在这项研究中，作者希望证明，当乳腺 X 光摄影与智能分割技术相结合时，它可以在诊断乳房异常和辅助早期发现乳腺癌方面变得更加有效。通过与智能分割技术结合，乳腺 X 光摄影可以更有效地诊断乳房异常，并辅助早期诊断乳腺癌，从而提高其整体效果。在完成对乳腺癌的文献综述（类别、涉及环境和生活方式的预防、诊断和疾病跟踪）后，将在以下部分描述该方法（神经网络和随机森林）。通过使用以前存在可疑区域（根据给定技术）的图像集，可以实现这些结果。Fiji 软件从乳腺 X 光图像中提取有问题的候选区域，然后对这些区域进行进一步检查。为了对图像分割的结果进行分类，将其分为三组：随机森林和神经网络都在突出显示图像中的可疑部分的分割中产生了有希望的结果，这两种算法都是如此。执行了区域轮廓的检测，表明可以创建这些分割部分的切割。之后，可以使用学习算法对目标进行自动分类，如实验所示。
