Department of Computer Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran.
Department of Computer Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
Comput Intell Neurosci. 2023 Nov 2;2023:7510419. doi: 10.1155/2023/7510419. eCollection 2023.
Medical intelligence detection systems have changed with the help of artificial intelligence and have also faced challenges. Breast cancer diagnosis and classification are part of this medical intelligence system. Early detection can lead to an increase in treatment options. On the other hand, uncertainty is a case that has always been with the decision-maker. The system's parameters cannot be accurately estimated, and the wrong decision is made. To solve this problem, we have proposed a method in this article that reduces the ignorance of the problem with the help of Dempster-Shafer theory so that we can make a better decision. This research on the MIAS dataset, based on image processing machine learning and Dempster-Shafer mathematical theory, tries to improve the diagnosis and classification of benign, malignant masses. We first determine the results of the diagnosis of mass type with MLP by using the texture feature and CNN. We combine the results of the two classifications with Dempster-Shafer theory and improve its accuracy. The obtained results show that the proposed approach has better performance than others based on evaluation criteria such as accuracy of 99.10%, sensitivity of 98.4%, and specificity of 100%.
医学智能检测系统在人工智能的帮助下发生了变化，同时也面临着挑战。乳腺癌的诊断和分类就是这个医学智能系统的一部分。早期发现可以增加治疗的选择。另一方面，不确定性一直是决策者面临的一个情况。系统的参数无法准确估计，从而导致错误的决策。为了解决这个问题，我们在本文中提出了一种方法，借助 Dempster-Shafer 理论来减少问题的不确定性，以便我们能够做出更好的决策。这项基于图像处理机器学习和 Dempster-Shafer 数学理论的 MIAS 数据集研究，试图提高良性、恶性肿块的诊断和分类。我们首先使用纹理特征和 CNN 确定 MLP 对肿块类型的诊断结果。我们将两种分类的结果结合起来，通过 Dempster-Shafer 理论来提高其准确性。获得的结果表明，该方法在准确性为 99.10%、灵敏度为 98.4%和特异性为 100%等评估标准下，比其他方法具有更好的性能。
