An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia.

Pradeep Kumar Das, Sukadev Meher, Adyasha Rath, Ganapati Panda
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Abstract

Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learning, is gaining a preferred trend of research in medical image processing because of their admirable performance, even with small datasets. It inspires us to develop a novel deep learning-based leukemia detection system in which an efficient and lightweight MobileNetV2 is used in conjunction with ShuffleNet to boost discrimination ability and enhance the receptive field via convolution layer succession. More importantly, the suggested weight factor and an optimal threshold value (which is experimentally selected) is responsible for maintaining a healthy balance between computational efficiency and classification performance. Hence, the benefits of inverted residual bottleneck structure, depthwise separable convolution, tunable hyperparameters, pointwise group convolution, and channel shuffling are integrated to improve the feature discrimination ability and make the proposed system faster and more accurate. The experimental results convey that the proposed framework outperforms others with the best detection performances. It achieves superior performance to its competitors with the best accuracy (99.07%), precision(98.00%), sensitivity (100%), specificity (98.31%), and F1 score (0.9899) in ALLIDB1 dataset. Similarly, it outperforms others with 98.46% accuracy, 98.46% precision, 98.46% specificity, 98.46% sensitivity, and 0.9846 F1 Score in ALLIDB2 dataset.

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用于急性淋巴细胞白血病自动检测的高效深度学习系统。
早期和高度准确地发现急性淋巴细胞白血病(ALL)等破坏性迅速的致命疾病,对于提供适当治疗以挽救宝贵生命至关重要。深度学习的最新发展,特别是迁移学习,由于其令人钦佩的性能,即使在小数据集上,也正在获得医学图像处理研究的首选趋势。这启发我们开发了一种新的基于深度学习的白血病检测系统,该系统将高效轻量级的MobileNetV2与ShuffleNet结合使用,通过卷积层的继承来提高识别能力和增强感受野。更重要的是,建议的权重因子和最优阈值(实验选择)负责在计算效率和分类性能之间保持健康的平衡。因此,综合了倒转残差瓶颈结构、深度可分卷积、可调超参数、点向群卷积和信道变换的优点,提高了特征识别能力,使系统更快、更准确。实验结果表明,该框架具有较好的检测性能。在ALLIDB1数据集上,其准确度(99.07%)、精密度(98.00%)、灵敏度(100%)、特异度(98.31%)和F1评分(0.9899)均优于竞争对手。同样,它在ALLIDB2数据集中的准确度为98.46%,精密度为98.46%,特异性为98.46%,灵敏度为98.46%,F1 Score为0.9846,优于其他方法。
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