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Enhancing leukemia detection in medical imaging using deep transfer learning.

Afeez A Soladoye¹, David B Olawade², Ibrahim A Adeyanju¹

¹Department of Computer Engineering, Federal University, Oye-Ekiti, Nigeria.

International Journal of Medical Informatics

|June 29, 2025

Summary

Deep transfer learning models like EfficientNet-B3 show promise for early Acute Lymphoblastic Leukemia (ALL) detection. EfficientNet-B3 achieved 96% accuracy, significantly outperforming VGG-19, offering a computationally efficient diagnostic tool.

Keywords:

Acute lymphoblastic leukemia Cancer Deep transfer learning EfficientNet-B3 Medical image classification

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Area of Science:

Medical Imaging
Computational Biology
Oncology

Background:

Acute Lymphoblastic Leukemia (ALL) is a prevalent pediatric cancer.
Early detection of ALL is crucial for improving patient outcomes and reducing treatment costs.
Traditional diagnostic methods for ALL can be time-consuming and resource-intensive.

Purpose of the Study:

To evaluate the efficacy of deep transfer learning algorithms for the early detection of Acute Lymphoblastic Leukemia (ALL).
To compare the performance of VGG-19 and EfficientNet-B3 models in classifying ALL from medical images.
To identify a computationally efficient and accurate method for ALL diagnosis.

Main Methods:

Utilized a public dataset of 10,661 images from 118 patients diagnosed with ALL.
Applied two transfer learning algorithms: VGG-19 and EfficientNet-B3.
Preprocessed data through resizing, augmentation, and normalization, training models for 100 epochs.

Main Results:

EfficientNet-B3 achieved a significantly higher average accuracy of 96% compared to VGG-19's 80% (p < 0.001).
EfficientNet-B3 demonstrated superior performance in handling class imbalance, with high precision, recall, and F1 scores for the minority class.
VGG-19 exhibited lower performance, particularly with the minority class, indicating challenges in handling imbalanced datasets.

Conclusions:

EfficientNet-B3 is a highly accurate and computationally efficient tool for early ALL detection.
Clinical integration necessitates addressing computational and integration challenges.
Future research should explore multimodal datasets to enhance risk factor identification and diagnostic accuracy.