Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification - Houssein, Essam H.; Mohamed, Osama; Abdel Samee, Nagwan; Mahmoud, Noha F.; Talaat, Rawan; Al-Hejri, Aymen M.; Al-Tam, Riyadh M. (Frontiers in Oncology, 2023)
Background: 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 is 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.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 white blood cell 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 deep learning models to boost training performance.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 2497, This is a provisional file, not the final typeset article 2483, 2487, and 2499. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.Using a combination of the recently developed pretrained CNN, DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of white blood cells for Leukemia detection. The proposed method is more accurate compared to the state of the art.