Page 72 - AIH-2-3

Artificial Intelligence in Health CNN model for leukemia diagnosis

Table 1. Literature review table summarizing work involving deep learning, the C‑NMC dataset, and leukemia
Title Authors Year Methodology Technology Conclusions
Leukemia classification using Arivuselvam and Sudha 4 2022 ResNet-34 and DenseNet-121 DCNN High accuracy in
the deep learning method of architectures for leukemia low-intensity images
CNN type classification classification
Leukemia Classification Kadhim et al. 5 2023 CNN achieving over 98% CNN Demonstrates the potential
using a Convolutional Neural classification accuracy of CNN for multilevel
Network of AML Images leukemia classification
Machine learning in detection Talaat and Gamel 6 2023 Image preprocessing, feature Optimized Achieved 99.99% accuracy
and classification of leukemia extraction, and classification CNN in classifying microscopic
using C-NMC_Leukemia with fuzzy optimization images
Optimizing a Deep Residual Rodrigues et al. 7 2022 Genetic algorithm combined Hybrid CNN Achieved 98.46% accuracy,
Neural Network with with ResNet-50V2 for with GA showcasing potential
Genetic Algorithm for Acute hyperparameter optimization for accurate leukemia
Lymphoblastic Leukemia diagnosis
Classification
Convergent learning– Mallick et al. 8 2020 Five-layer DNN classifier for DNN High accuracy in leukemia
based model for leukemia gene expression data classification using gene
classification from gene expression datasets
expression
Automatic Detection Arif et al. 9 2022 Modified CNN model for data CNN High accuracy and
of Leukemia through augmentation, segmentation, reliability, suitable for
Convolutional Neural Network and classification clinical applications
A hybrid detection model for Alsaykhan and Maashi 10 2024 Hybrid detection model using SVM-PSO High accuracy for acute
acute lymphocytic leukemia SVM and particle swarm lymphocytic leukemia
using SVM-PSO optimization detection
Ensemble learning using Abhishek et al. 11 2025 Ensemble learning framework Ensemble Improved classification
Gompertz function for leveraging Gompertz function learning accuracy for leukemia
leukemia classification diagnosis
Deep Transfer Learning in Loey et al. 12 2020 Transfer learning-based Transfer Accurate blood cell
Diagnosing Leukemia in framework learning leukemia diagnosis
Blood Cells
Navigating Tversky Loss Damit et al. 13 2024 Optimizing hyperparameters Particle Enhanced segmentation
Function Hyperparameter of Tversky loss for swarm performance
Spaces using Particle Swarm segmentation optimization
Optimization
Segmentation and Kumar and Rawat 14 2024 Modified CNN for Modified Accurate classification of
classification of white blood segmentation and CNN white blood smear images
smear images using modified classification
CNN architecture
Machine Learning Muhsen et al. 15 2020 ML applications for ML Effective for benign and
Applications in the Diagnosis hematological disease Framework malignant hematological
of Benign and Malignant diagnosis disease diagnosis
Hematological Diseases
Explainable AI identifies Hehr et al. 16 2023 Explainable AI framework for Explainable High precision in
diagnostic cells of genetic identifying diagnostic cells AI identifying AML subtypes
AML subtypes
Hyperparameter Antunes et al. 17 2023 CNN hyperparameter Optimized Enhanced generalization
Optimization of a optimization using various CNN across applications
Convolutional Neural search techniques
Network Model for Pipe Burst
Location
Metalearning approach for Rodrigues and 2020 Meta-learning framework for Meta-learning Effective in identifying
leukemia informative genes Deusdado 18 gene prioritization leukemia-informative
prioritization genes
(Cont’d...)

Volume 2 Issue 3 (2025) 66 doi: 10.36922/aih.4710