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

Abstract

BackgroundThe 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 MethodsImages 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.ResultsThe 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.ConclusionUsing 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.

A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review

“…The year 2023 demonstrated a focus on using only one model for feature extraction and classification; four articles published in this year were included in this review [ 37 - 40 ]. Houssein and colleagues attained 99.80% accuracy with DenseNet-161 (a model from densely connected convolutional networks) using augmentation, segmentation, and RGB (R: red, G: green, B: blue) to HSV (H: hue, S: saturation, V: value) [ 37 ], while other researchers averaged 98.15% accuracy with a CNN model using only image segmentation [ 38 ]. Naz and colleagues achieved 96.9% and 81.9% on separate datasets using AlexNet by augmenting their data and segmenting their images [ 39 ].…”

“…In 2023, an important shift occurred from using several CNNs for specific tasks to applying a single model for feature extraction and cell classification, with DenseNet-161 and AlexNet achieving 99.80% and 96.90%/81.90%, accuracy, respectively [ 37 - 40 ]. However, the highly variable accuracy rates demonstrate the need for more research and replication of these studies.…”

Collaborative Federated Learning-Based Model for Alert Correlation and Attack Scenario Recognition

“…In addition, FL can be used to satisfy other design requirements, such as privacy, data diversity, real-time model updates, and improved prediction. In future work, it is recommended to explore the utilization of various ensemble DL models [43], hybrid models of CNN [44], XAI [45], and transformer models [46] in combination with federated learning. In addition, we will broaden our analysis to include real-world network traffic.…”