Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
Department of Biotechnology, College of Science, Engineering and Technology, Osun State University, Osogbo, Nigeria.
Int J Lab Hematol. 2024 Oct;46(5):837-849. doi: 10.1111/ijlh.14305. Epub 2024 May 10.
Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells.
To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis.
The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation.
The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.
急性淋巴细胞白血病（ALL）是血液系统恶性肿瘤中的一个严峻挑战，需要快速准确的诊断技术来进行有效的干预。传统的血液涂片手工显微镜检查虽然广泛应用，但存在着显著的局限性，包括劳动强度大，容易出现人为错误，尤其是在区分正常细胞和白血病细胞的细微差异方面。
为了克服这些局限性，我们的研究引入了 ALLDet 分类器，这是一种创新的工具，利用深度迁移学习对白细胞（WBC）核图像进行 ALL 的自动分析和分类。我们的研究包括评估九个最先进的预训练卷积神经网络（CNN）模型，即 VGG16、VGG19、ResNet50、ResNet101、DenseNet121、DenseNet201、Xception、MobileNet 和 EfficientNetB3。我们通过引入一种复杂的基于轮廓的分割技术，从 Chan-Vese 模型中得到，旨在对血液涂片图像中的 blast 细胞核进行细致的分割，从而提高了我们的分析准确性，来增强这种方法。
这些方法的实证评估强调了 EfficientNetB3 模型的卓越性能，该模型表现出出色的指标：召回特异性为 98.5%，精度为 95.86%，F1 得分为 97.16%，整体准确率为 97.13%。Chan-Vese 模型对 blast 细胞不规则形状的适应性及其抗噪分割能力是捕捉准确分割所需的复杂形态变化的关键。
ALLDet 分类器与我们的先进分割方法相结合，以 EfficientNetB3 为动力，这是 ALL 早期检测和准确诊断方面的一项重大进展。这一突破不仅标志着白血病诊断方法学的一个重要飞跃，而且还通过提供及时准确的诊断，有望显著提高患者护理的标准。这项研究的意义不仅限于直接的临床应用，还为人工智能在医学诊断中的进一步改进和增强能力的未来研究铺平了道路。
