Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Essam H Houssein; Osama Mohamed; Nagwan Abdel Samee; Noha F Mahmoud; Rawan Talaat; Aymen M Al-Hejri; Riyadh M Al-Tam

doi:10.3389/fonc.2023.1230434

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Front Oncol. 2023 Sep 12:13:1230434. doi: 10.3389/fonc.2023.1230434. eCollection 2023.

Authors

Essam H Houssein¹, Osama Mohamed², Nagwan Abdel Samee³, Noha F Mahmoud⁴, Rawan Talaat⁵, Aymen M Al-Hejri⁶, Riyadh M Al-Tam⁶

Affiliations

¹ Faculty of Computers and Information, Minia University, Minia, Egypt.
² Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
³ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁴ Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁵ Biotechnology & Genetics Department, Agriculture Engineering, Ain Shams University, Cairo, Egypt.
⁶ School of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Ma-harashtra, India.

Abstract

Background: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification.

Materials and methods: Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance.

Results: The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.

Conclusion: Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

Keywords: DenseNet; cyclical learning rate; leukemia; leukocytes; transfer learning.

Grants and funding

This work was supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R206), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.