PDF(Pubmed)
Abstract：
Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model\'s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model\'s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.
摘要：
白血病是一种明确影响血细胞的恶性疾病，导致危及生命的感染和过早死亡。最先进的机器支持技术和复杂的深度学习算法可以帮助临床医生进行早期疾病诊断。这项研究介绍了一种先进的端到端方法，用于自动诊断急性白血病类急性淋巴细胞白血病（ALL）和急性髓细胞性白血病（AML）。这项研究收集了44名患者的完整数据库，包括670个ALL和AML图像。提出的深度模型的架构由图论和卷积神经网络(CNN)的融合组成，具有六个图形Conv层和一个Softmax层。所提出的深度模型对于ALL和AML类别实现了99%的分类准确度和0.85的kappa系数。建议的模型在嘈杂的条件下进行了评估，并表现出了很强的弹性。具体来说,模型的准确率保持在90%以上，即使在信噪比(SNR)为0dB的情况下。根据当代方法和研究对拟议的方法进行了评估，展示令人鼓舞的结果。据此,建议的深度模型可以作为临床医生识别急性白血病特定形式的工具.
