Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India.
Sensors (Basel). 2022 Feb 19;22(4):1629. doi: 10.3390/s22041629.
Leukemia is one of the most dangerous types of malignancies affecting the bone marrow or blood in all age groups, both in children and adults. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL). It is diagnosed by hematologists and experts in blood and bone marrow samples using a high-quality microscope with a magnifying lens. Manual diagnosis, however, is considered slow and is limited by the differing opinions of experts and other factors. Thus, this work aimed to develop diagnostic systems for two Acute Lymphoblastic Leukemia Image Databases (ALL_IDB1 and ALL_IDB2) for the early detection of leukemia. All images were optimized before being introduced to the systems by two overlapping filters: the average and Laplacian filters. This study consists of three proposed systems as follows: the first consists of the artificial neural network (ANN), feed forward neural network (FFNN), and support vector machine (SVM), all of which are based on hybrid features extracted using Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM) and Fuzzy Color Histogram (FCH) methods. Both ANN and FFNN reached an accuracy of 100%, while SVM reached an accuracy of 98.11%. The second proposed system consists of the convolutional neural network (CNN) models: AlexNet, GoogleNet, and ResNet-18, based on the transfer learning method, in which deep feature maps were extracted and classified with high accuracy. All the models obtained promising results for the early detection of leukemia in both datasets, with an accuracy of 100% for the AlexNet, GoogleNet, and ResNet-18 models. The third proposed system consists of hybrid CNN-SVM technologies, consisting of two blocks: CNN models for extracting feature maps and the SVM algorithm for classifying feature maps. All the hybrid systems achieved promising results, with AlexNet + SVM achieving 100% accuracy, Goog-LeNet + SVM achieving 98.1% accuracy, and ResNet-18 + SVM achieving 100% accuracy.
白血病是一种最危险的恶性肿瘤类型，影响所有年龄段的骨髓或血液，包括儿童和成人。最危险和致命的白血病类型是急性淋巴细胞白血病（ALL）。血液学家和血液及骨髓样本专家使用高质量显微镜和放大镜头进行诊断。然而，手动诊断被认为速度较慢，并受到专家意见和其他因素的差异限制。因此，这项工作旨在为两个急性淋巴细胞白血病图像数据库（ALL_IDB1 和 ALL_IDB2）开发诊断系统，以早期发现白血病。所有图像在引入系统之前都经过了两种重叠滤波器的优化：平均滤波器和拉普拉斯滤波器。本研究包括以下三个拟议系统：第一个系统由人工神经网络（ANN）、前馈神经网络（FFNN）和支持向量机（SVM）组成，它们都是基于使用局部二值模式（LBP）、灰度共生矩阵（GLCM）和模糊颜色直方图（FCH）方法提取的混合特征。ANN 和 FFNN 的准确率均达到 100%，而 SVM 的准确率达到 98.11%。第二个拟议系统由卷积神经网络（CNN）模型组成：AlexNet、GoogleNet 和 ResNet-18，基于迁移学习方法，提取和分类深度特征图，准确率高。所有模型在两个数据集的白血病早期检测中均取得了有前景的结果，AlexNet、GoogleNet 和 ResNet-18 模型的准确率为 100%。第三个拟议系统由混合 CNN-SVM 技术组成，包括两个模块：用于提取特征图的 CNN 模型和用于分类特征图的 SVM 算法。所有混合系统都取得了有前景的结果，其中 AlexNet+SVM 达到 100%的准确率，Goog-LeNet+SVM 达到 98.1%的准确率，ResNet-18+SVM 达到 100%的准确率。
