用深度学习区分白血病四阶段，准确率达99.7%。
Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
- 基于MobileNetV2和自定义CNN模型，结合迁移学习
- 在ALL图像数据集上实现99.69%的分类准确率
- 适用于临床早期诊断，尤其适合医学影像分析者
单个细胞的DNA突变可引发白血病，导致未成熟白血球过度生成，挤占正常血细胞生成空间。若能在早期发现，白血病可被有效治疗，但传统诊断过程繁琐耗时。本研究提出一种新方法，利用深度学习技术对白血病四个阶段（正常、早期、前驱、进展期）进行检测与分类。采用两种卷积神经网络模型：修改头部的MobileNetV2与自定义模型（多组卷积层+最大池化层）。使用公开的“急性淋巴细胞白血病（ALL）图像数据集”，并应用合成少数类过采样技术（SMOTE）平衡训练数据。自定义模型准确率为98.6%，而MobileNetV2达到99.69%的更高准确率。预训练模型表现优异，显示出较强的现实应用潜力。
原文摘要 · Abstract (English)
A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells that encroach upon the space required for the generation of healthy blood cells.Leukemia is treatable if identified in its initial stages. However,its diagnosis is both arduous and time consuming. This study proposes a novel approach for diagnosing leukemia across four stages Benign,Early,Pre,and Pro using deep learning techniques.We employed two Convolutional Neural Network (CNN) models as MobileNetV2 with an altered head and a custom model. The custom model consists of multiple convolutional layers,each paired with corresponding max pooling layers.We utilized MobileNetV2 with ImageNet weights,adjusting the head to integrate the final results.The dataset used is the publicly available "Acute Lymphoblastic Leukemia (ALL) Image Dataset", and we applied the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset.The custom model achieved an accuracy of 98.6%, while MobileNetV2 attained a superior accuracy of 99.69%. The pretrained model showed promising results,indicating an increased likelihood of real-world application.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。