Moradi Mohammad, Rezai Abdalhossein
ACECR Institute of Higher Education, Isfahan Branch, Isfahan, Iran.
Department of Electrical Engineering, 577243 University of Science and Culture , Tehran, Iran.
Biomed Tech (Berl). 2024 Dec 23;70(2):171-181. doi: 10.1515/bmt-2024-0185. Print 2025 Apr 28.
One of the primary causes of the women death is breast cancer. Accurate and early breast cancer diagnosis plays an essential role in its treatment. Computer Aided Diagnosis (CAD) system can be used to help doctors in the diagnosis process. This study presents an efficient method to performance improvement of the breast cancer diagnosis CAD system using thermal images.
The research strategy in the proposed CAD system is using efficient algorithms in feature extraction and classification phases, and new efficient feature selection algorithm. In the feature extraction phase, the Segmentation Fractal Texture Analysis (SFTA) algorithm that is a texture analysis algorithm is used.This algorithm utilizes two-threshold binary decomposition. In the feature selection phase, the developed feature selection algorithm, which is hybrid of binary grey wolf optimization algorithm and firefly optimization algorithm, is applied to extracted features. Then, the kNN, SVM, and DTree classification techniques are applied to check whether the selected features are efficiently discriminated the group successfully with minimal misclassifications.
The DMR database is utilized for performance evaluation of the proposed method. The results indicate that the obtained accuracy, specificity, sensitivity, and MCC are 97, 96, 98, and 94.17 %, respectively.
The developed breast cancer diagnosis method has advantages compared to other breast cancer diagnosis using thermal images.
女性死亡的主要原因之一是乳腺癌。准确且早期的乳腺癌诊断在其治疗中起着至关重要的作用。计算机辅助诊断（CAD）系统可用于在诊断过程中帮助医生。本研究提出了一种使用热图像提高乳腺癌诊断CAD系统性能的有效方法。
所提出的CAD系统中的研究策略是在特征提取和分类阶段使用高效算法，以及新的高效特征选择算法。在特征提取阶段，使用了一种纹理分析算法——分割分形纹理分析（SFTA）算法。该算法利用双阈值二进制分解。在特征选择阶段，将开发的特征选择算法（二进制灰狼优化算法和萤火虫优化算法的混合算法）应用于提取的特征。然后，应用kNN、SVM和DTree分类技术来检查所选特征是否能以最小的错误分类成功有效地区分组。
利用DMR数据库对所提方法进行性能评估。结果表明，所获得的准确率、特异性、灵敏度和马修斯相关系数分别为97%、96%、98%和94.17%。
与其他使用热图像的乳腺癌诊断方法相比，所开发的乳腺癌诊断方法具有优势。
