ArticleJournal of digital imaging2022
Optimizing a Deep Residual Neural Network with Genetic Algorithm for Acute Lymphoblastic Leukemia Classification.
Article in Journal of digital imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 35 citations in OpenAlex.
- Development and Evaluation of an Automated Histomorphometric Analysis Method for the Assessment of Implant Osseointegration.Journal of imaging informatics in medicine · 2026Article
- BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset.Journal of imaging informatics in medicine · 2025Article
- Article
- An explainable deep learning-based feature fusion model for acute lymphoblastic leukemia diagnosis and severity assessment.Frontiers in medicine · 2025Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.Scientific reports · 2024Article
- Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Computational design of clinical trials using a combination of simulation and the genetic algorithm.CPT: pharmacometrics & systems pharmacology · 2023Article
- An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.Applied soft computing · 2023Article
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
- Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review.Neural processing letters · 2022Review
- Entropy Measurements for Leukocytes' Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide, and it is characterized by the production of immature malignant cells in the bone marrow. Computer vision techniques provide automated analysis that can help specialists diagnose this disease. Microscopy image analysis is the most economical method for the initial screening of patients with ALL, but this task is subjective and time-consuming. In this study, we propose a hybrid model using a genetic algorithm (GA) and a residual convolutional neural network (CNN), ResNet-50V2, to predict ALL using microscopy images available in ALL-IDB dataset. However, accurate prediction requires suitable hyperparameters setup, and tuning these values manually still poses challenges. Hence, this paper uses GA to find the best hyperparameters that lead to the highest accuracy rate in the models. Also, we compare the performance of GA hyperparameter optimization with Random Search and Bayesian optimization methods. The results show that GA optimization improves the accuracy of the classifier, obtaining 98.46% in terms of accuracy. Additionally, our approach sheds new perspectives on identifying leukemia based on computer vision strategies, which could be an alternative for applications in a real-world scenario.
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Registered trials
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