doi: 10.18178/JAAI.2024.2.1.108-121
Blood Cells Cancer Detection Based on Deep Learning
Ahmed J. Abougarair
1,*
,
,
,
,
,
,
- 1 1 Electrical and Electronics Engineering, University of Tripoli, Libya
- 2 2 Faculty of Engineering, Sabratha University, Sabratha, Libya
- 3 3 School of Computational Sciences, S.R.T.M. University, Nanded, India
- 4 4 Department of Information System Management, Sanaa University, Sana’a Yemen
- 5 5 Department of Computer Engineering, Faculty of Engineering Ege University, Turkey
- Manuscript received January 10, 2024
- accepted March 8, 2024
- published May 21, 2024
Abstract
Acute Lymphocytic Leukemia (ALL) is a form of cancer characterized by the abnormal production of white blood cells in the bone marrow. These cells do not function properly, leading to the overcrowding of healthy cells and weakening the body's immune system, making it more susceptible to infections. ALL progresses rapidly in children, and without timely treatment, it can be fatal. However, manually detecting this disease is a time-consuming and laborious task. In contrast, machine learning and deep learning techniques offer faster and more accurate detection methods. This study proposes a deep feature selection approach for identifying Acute Lymphocytic Leukemia in images of peripheral blood specimens. The approach utilizes the MobileNetV2 model to extract deep features from a dataset of peripheral blood specimen images, which are then used to train the model. By leveraging the base structure of MobileNetV2, the model demonstrates a high level of accuracy. Furthermore, by incorporating activation functions and additional layers into the model, the accuracy is significantly improved..
Keywords
- Blood cell
- Cancer
- CNN
- deep learning
- MobileNetV2
How to Cite
Ahmed J. Abougarair, M. Alshaibi, Amany K. Alarbish, Mohammed Ali Qasem, Doaa Abdo Othman Qasem, Fursan Thabit, and Ozgu Can, "Blood Cells Cancer Detection Based on Deep Learning," Journal of Advances in Artificial Intelligence, vol. 2, no. 1, pp. 108-121, 2024. https://doi.org/ 10.18178/JAAI.2024.2.1.108-121
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).