Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

Abstract

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

Evaluation of Antioxidant and Anti-anaemic Potential of Moringa oleifera Parts on Phenylhydrazine-induced Heamatotoxicity in Male Wistar Albino Rats

“…These agreed with the elevated white blood cells in PHZ rats in the study on haematological parameters. Lymphoblastic leukaemia is associated with high WBC production in response to injuries induced by toxic compounds (Ahmed et al, 2023). The treatment groups exhibit some diffused cells across groups and minor myeloblastic and lymphoblastic cellular characteristics, which is a sign that Moringa oleifera root, bark, seed, leaves and flower had an ameliorative effect on the bone marrow.…”

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning‐based techniques

“…To clean up these images, filters are needed to smoothen or reduce the prominence of background pixels while amplifying the regions of interest. This process ultimately enhances the quality of the segmentation results 22 …”

“…To clean up these images, filters are needed to smoothen or reduce the prominence of background pixels while amplifying the regions of interest. This process ultimately enhances the quality of the segmentation results 22. This research utilizes the Adaptive Median Filtering technique to minimize the presence of noises in the images.…”

Multihead Neural Network for Multiple Segmented Images-Based Diagnosis of Thyroid-Associated Orbitopathy Activity

“…For example, Con-vNeXt, which was initially developed for natural images, has been extended to predict the severity of lung damage [19]. In addition, three traditional CNNs were utilized to diagnose acute lymphoblastic leukemia, with their extracted spatial features fed into either an extreme gradient boosting classifier [20]. The versatility of CNNs is extended to various diagnostic objectives, including breast cancer diagnosis [21], facial skin condition identification [22], and brain tumor detection [23].…”