文章基本信息

标题：Deep Transfer Learning in Diagnosing Leukemia in Blood Cells
本地全文：下载
作者：Mohamed Loey ; Mukdad Naman ; Hala Zayed 等
期刊名称：Computers
电子版ISSN：2073-431X
出版年度：2020
卷号：9
期号：2
页码：29-40
DOI：10.3390/computers9020029
出版社：MDPI Publishing
摘要：Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.
关键词：deep learning; leukemia detection; transfer learning deep learning ; leukemia detection ; transfer learning