Next Article in Journal
Eliminating Nonuniform Smearing and Suppressing the Gibbs Effect on Reconstructed Images
Next Article in Special Issue
Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting
Previous Article in Journal
Insights into Mapping Solutions Based on OPC UA Information Model Applied to the Industry 4.0 Asset Administration Shell
Open AccessArticle

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Computer Science Department, Faculty of Computer Artificial Intelligence, Benha University, Benha 13511, Egypt
Author to whom correspondence should be addressed.
Computers 2020, 9(2), 29;
Received: 4 February 2020 / Revised: 18 March 2020 / Accepted: 18 March 2020 / Published: 15 April 2020
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. View Full-Text
Keywords: deep learning; leukemia detection; transfer learning deep learning; leukemia detection; transfer learning
Show Figures

Figure 1

MDPI and ACS Style

Loey, M.; Naman, M.; Zayed, H. Deep Transfer Learning in Diagnosing Leukemia in Blood Cells. Computers 2020, 9, 29.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop