El Houby Enas M F
Systems & Information Department, National Research Centre, Dokki, Cairo, 12311, Egypt.
Sci Rep. 2025 Aug 1;15(1):28056. doi: 10.1038/s41598-025-12361-4.
Cancer is considered one of the deadliest diseases worldwide. Early detection of cancer can significantly improve patient survival rates. In recent years, computer-aided diagnosis (CAD) systems have been increasingly employed in cancer diagnosis through various medical image modalities. These systems play a critical role in enhancing diagnostic accuracy, reducing physician workload, providing consistent second opinions, and contributing to the efficiency of the medical industry. Acute lymphoblastic leukemia (ALL) is a fast-progressing blood cancer that primarily affects children but can also occur in adults. Early and accurate diagnosis of ALL is crucial for effective treatment and improved outcomes, making it a vital area for CAD system development. In this research, a CAD system for ALL diagnosis has been developed. It contains four phases which are preprocessing, segmentation, feature extraction and selection phase, and classification of suspicious regions as normal or abnormal. The proposed system was applied to microscopic blood images to classify each case as ALL or normal. Three classifiers which are Naïve Bayes (NB), Support Vector Machine (SVM) and K-nearest Neighbor (K-NN) were utilized to classify the images based on selected features. Ant Colony Optimization (ACO) was combined with the classifiers as a feature selection method to identify the optimal subset of features among the extracted features from segmented cell parts that yield the highest classification accuracy. The NB classifier achieved the best performance, with accuracy, sensitivity, and specificity of 96.15%, 97.56, and 94.59%, respectively.
癌症被认为是全球最致命的疾病之一。癌症的早期检测可以显著提高患者的生存率。近年来，计算机辅助诊断（CAD）系统已通过各种医学图像模态越来越多地应用于癌症诊断。这些系统在提高诊断准确性、减轻医生工作量、提供一致的第二意见以及提高医疗行业效率方面发挥着关键作用。急性淋巴细胞白血病（ALL）是一种进展迅速的血癌，主要影响儿童，但也可能发生在成人身上。ALL的早期准确诊断对于有效治疗和改善预后至关重要，使其成为CAD系统开发的一个重要领域。在这项研究中，开发了一种用于ALL诊断的CAD系统。它包含四个阶段，即预处理、分割、特征提取与选择阶段，以及将可疑区域分类为正常或异常。所提出的系统应用于微观血液图像，以将每个病例分类为ALL或正常。使用朴素贝叶斯（NB）、支持向量机（SVM）和K近邻（K-NN）这三种分类器基于所选特征对图像进行分类。蚁群优化（ACO）与分类器相结合作为一种特征选择方法，以从分割的细胞部分提取的特征中识别出能产生最高分类准确率的最优特征子集。NB分类器表现最佳，准确率、灵敏度和特异性分别为96.15%、97.56和94.59%。
