Acute lymphoblastic leukemia (ALL) is the most malignant form of leukemia and the most common cancer in adults and children. Traditionally, leukemia is diagnosed by analyzing blood and bone marrow smears under a microscope, with additional cytochemical tests for confirmation. However, these methods are expensive, time consuming, and highly dependent on expert knowledge. In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has provided advanced methods for classifying microscopic smear images, aiding in the detection of leukemic cells. These approaches are quick, cost effective, and not subject to human bias. However, most methods lack the ability to quantify uncertainty, which could lead to critical misdiagnoses. In this research, hybrid deep learning models (InceptionV3-GRU, EfficientNetB3-GRU, MobileNetV2-GRU) were implemented to classify ALL. Bayesian optimization was used to fine tune the model's hyperparameters and improve its performance. Additionally, Deep Ensemble uncertainty quantification was applied to address uncertainty during leukemia image classification. The proposed models were trained on the publicly available datasets ALL-IDB1 and ALL-IDB2. Their results were then aggregated at the score level using the sum rule. The parallel architecture used in these models offers a high level of confidence in differentiating between ALL and non-ALL cases. The proposed method achieved a remarkable detection accuracy rate of 100% on the ALL-IDB1 dataset, 98.07% on the ALL-IDB2 dataset, and 98.64% on the combined dataset, demonstrating its potential for accurate and reliable leukemia diagnosis.

翻译：急性淋巴细胞白血病（ALL）是最恶性的白血病类型，也是成人和儿童中最常见的癌症。传统上，白血病通过显微镜下分析血液和骨髓涂片进行诊断，并辅以细胞化学检测进行确认。然而，这些方法成本高昂、耗时较长，且高度依赖专家知识。近年来，深度学习技术，特别是卷积神经网络（CNNs），为显微涂片图像分类提供了先进方法，有助于白血病细胞的检测。这些方法快速、经济且不受人为偏差影响。然而，大多数方法缺乏不确定性量化能力，可能导致严重的误诊。本研究实现了混合深度学习模型（InceptionV3-GRU、EfficientNetB3-GRU、MobileNetV2-GRU）用于ALL分类。采用贝叶斯优化对模型超参数进行微调以提升性能。此外，应用深度集成不确定性量化方法来解决白血病图像分类过程中的不确定性问题。所提出的模型在公开数据集ALL-IDB1和ALL-IDB2上进行训练，并通过求和规则在分数层面融合其结果。这些模型采用的并行架构为区分ALL与非ALL病例提供了高置信度。该方法在ALL-IDB1数据集上实现了100%的显著检测准确率，在ALL-IDB2数据集上达到98.07%，在合并数据集上达到98.64%，证明了其在实现准确可靠白血病诊断方面的潜力。