Enhancing leukemia detection in medical imaging using deep transfer learning

Abstract

Background

Acute Lymphoblastic Leukemia (ALL) is the most common pediatric cancer, requiring early detection to save lives and reduce the financial burden of advanced-stage treatment. While traditional diagnostic methods are time-consuming and resource-intensive, deep transfer learning offers a computationally efficient alternative for medical image classification.

Method

This study employed two widely recognized transfer learning algorithms, VGG-19 and EfficientNet-B3, to detect ALL using a publicly available dataset of 10,661 images from 118 patients. Data preprocessing included resizing, augmentation, and normalization. The models were trained for 100 epochs, with batch sizes of 30 for VGG-19 and 32 for EfficientNet-B3. Evaluation metrics such as accuracy, precision, recall, and F1 score were used to assess model performance. Statistical significance testing was performed using paired t-tests (p < 0.05). Comparative analysis was performed with existing studies to validate the findings.

Results

EfficientNet-B3 significantly outperformed VGG-19, achieving an average accuracy of 96 % compared to 80 % for VGG-19 (p < 0.001). EfficientNet-B3 demonstrated superior performance in handling class imbalance, with the minority class (Hem) achieving precision, recall, and F1 scores of 97 %, 89 %, and 93 %, respectively. VGG-19 struggled with the minority class, achieving lower recall (51 %) and F1 score (62 %). However, dataset limitations including single-source origin may affect generalizability.

Conclusion

This study highlights the effectiveness of EfficientNet-B3 as a reliable tool for early ALL detection, offering high accuracy and computational efficiency. Clinical implementation requires addressing computational constraints and integration challenges. Future research could integrate multimodal datasets to identify risk factors and further improve diagnostic accuracy.