Muhammad Dost, Salman Muhammad, Keles Ayse, Bendechache Malika
CRT-AI and ADAPT Research Centres, School of Computer Science, University of Galway, Galway, Ireland.
Department of Software Engineering, University of Malakand, Malakand, Pakistan.
Sci Rep. 2025 Apr 14;15(1):12812. doi: 10.1038/s41598-025-97297-5.
Acute Lymphoblastic Leukemia (ALL) is a life-threatening malignancy characterized by its aggressive progression and detrimental effects on the hematopoietic system. Early and accurate diagnosis is paramount to optimizing therapeutic interventions and improving clinical outcomes. This study introduces a novel diagnostic framework that synergizes the EfficientNet-B7 architecture with Explainable Artificial Intelligence (XAI) methodologies to address challenges in performance, computational efficiency, and explainability. The proposed model achieves improved diagnostic performance, with accuracies exceeding 96% on the Taleqani Hospital dataset and 95.50% on the C-NMC-19 and Multi-Cancer datasets. Rigorous evaluation across multiple metrics-including Area Under the Curve (AUC), mean Average Precision (mAP), Accuracy, Precision, Recall, and F1-score-demonstrates the model's robustness and establishes its superiority over state-of-the-art architectures namely VGG-19, InceptionResNetV2, ResNet50, DenseNet50 and AlexNet . Furthermore, the framework significantly reduces computational overhead, achieving up to 40% faster inference times, thereby enhancing its clinical applicability. To address the opacity inherent in Deep learning (DL) models, the framework integrates advanced XAI techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), Class Activation Mapping (CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Integrated Gradients (IG), providing transparent and explainable insights into model predictions. This fusion of high diagnostic precision, computational efficiency, and explainability positions the proposed framework as a transformative tool for ALL diagnosis, bridging the gap between cutting-edge AI technologies and practical clinical deployment.
急性淋巴细胞白血病（ALL）是一种危及生命的恶性肿瘤，其特征是进展迅速且对造血系统有不良影响。早期准确诊断对于优化治疗干预措施和改善临床结果至关重要。本研究引入了一种新颖的诊断框架，该框架将EfficientNet - B7架构与可解释人工智能（XAI）方法相结合，以应对性能、计算效率和可解释性方面的挑战。所提出的模型实现了更高的诊断性能，在Taleqani医院数据集上的准确率超过96%，在C - NMC - 19和多癌症数据集上的准确率为95.50%。通过包括曲线下面积（AUC）、平均平均精度（mAP）、准确率、精确率、召回率和F1分数在内的多个指标进行的严格评估，证明了该模型的稳健性，并确立了其相对于VGG - 19、InceptionResNetV2、ResNet50、DenseNet50和AlexNet等现有先进架构的优越性。此外，该框架显著减少了计算开销，推理时间加快了40%，从而提高了其临床适用性。为了解决深度学习（DL）模型固有的不透明性问题，该框架集成了先进的XAI技术，包括梯度加权类激活映射（Grad - CAM）、类激活映射（CAM）、局部可解释模型无关解释（LIME）和集成梯度（IG），为模型预测提供透明且可解释的见解。这种高诊断精度、计算效率和可解释性的融合，使所提出的框架成为ALL诊断的变革性工具，弥合了前沿人工智能技术与实际临床应用之间的差距。
