Deep Learning-Based Transfer Learning for the Detection of Leukemia

• Published in: 2023 International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) pp. 1 - 6
• Main Authors: Sheet, Joydeb, Ghosh, Chanchal, Das, Biplab Kanti
• Format: Conference Proceeding
• Language: English
• Published: IEEE 03.02.2023
• Subjects: ALL; CNN; Computational modeling; Convolutional neural networks; Deep learning; Medical imaging; Merging; Microscopy; Mobilenet V2; Predictive models; Sensitivity; Transfer learning
• DOI: 10.1109/ISACC56298.2023.10084138

Leukemia is typically diagnosed by using a microscope to examine blood and bone marrow smears as well as extremely challenging procedures like the Cytochemical test, which can be used to identify and categorize leukemia. However, these techniques are incredibly pricy, dependent, and time-consuming on the skill and cunning of the experts involved. In contrast to conventional methods, which have several drawbacks, this research suggests a classification model depends upon blood microscopic images for detecting leukemia by utilizing transfer learning. The suggested study demonstrates a computer-aided diagnosis method that uses Mobilenet V2 and Convolutional Neural Networks (CNNs) to identify leukemia images over healthy images. When it comes to using deep learning for image analysis and the comparison outcomes of the current study for diagnostic tasks, the implementation of Mobilenet V2 is relatively simple. Results demonstrate that the suggested model, having a general accuracy of 96.58%, a sensitivity of 95.17%, and a specificity of 98.58%, gives better predictions than separate models for classifying leukemia.