Abd El-Ghany Sameh, Elmogy Mohammed, El-Aziz Abd
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia.
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.
Diagnostics (Basel). 2023 Jan 22;13(3):404. doi: 10.3390/diagnostics13030404.
The immune system's overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model's average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model's average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models.
免疫系统白细胞（WBCs）过度产生会导致最常见的血癌——白血病。它约占儿童癌症的25%，是全球主要死因之一。在人类骨髓中发现的最著名的白血病类型是急性淋巴细胞白血病（ALL）。这是一种影响骨髓并杀死白细胞的疾病。早期精确的癌症检测有助于实现更好的治疗和更高的生存可能性。因此，医生可以使用计算机辅助诊断（CAD）模型来有效检测早期白血病。在本研究中，我们提出了一种基于EfficientNet - B3卷积神经网络（CNN）模型的分类模型，将ALL作为一种能自动改变学习率（LR）的自动化模型进行区分。我们设置了一个自定义LR，在每个epoch开始时比较损失值和训练准确率。我们在C - NMC_Leukemia数据集上评估了所提出的模型。该数据集经过了归一化和平衡预处理。对所提出的模型进行了评估，并与近期的分类器进行了比较。所提出模型的平均精度、召回率、特异性、准确率和盘状相似系数（DSC）分别为98.29%、97.83%、97.82%、98.31%和98.05%。此外，所提出的模型还用于检查血液的微观图像以识别疟原虫。我们所提出模型的平均精度、召回率、特异性、准确率和DSC分别为97.69%、97.68%、97.67%、97.68%和97.68%。因此，对所提出模型的评估表明，与其他现有模型相比，它在经过调优后具有无与伦比的感知效果。
