Comparative Analysis of ConvNeXt and EfficientNet-B0 for Early Leukemia Detection through Blood Cell Classification with Grad-CAM Interpretability
DOI:
https://doi.org/10.30871/jaic.v10i3.12816Keywords:
ConvNeXt, Deep Learning, EfficientNet-B0, Grad-CAM interpretability, Leukemia detectionAbstract
Leukemia is a hematological malignancy requiring early and accurate diagnosis for optimal patient outcomes, yet conventional microscopic examination remains subjective, time-consuming, and prone to inter-observer variability. This study presents a comprehensive comparative analysis of two state-of-the-art deep learning architectures EfficientNet-B0 and ConvNeXt-Tiny for multi-class blood cell classification aimed at early leukemia detection. Using a balanced dataset of 5,000 microscopic images encompassing five clinically significant classes (basophil, erythroblast, monocyte, myeloblast, and segmented neutrophil), both models were trained and evaluated under identical configurations with extensive data augmentation. Performance assessment encompassed classification metrics, inference speed, and interpretability through Gradient-weighted Class Activation Mapping (Grad-CAM) validated by randomization and occlusion tests. Results demonstrated that both architectures achieved exceptional performance with F1-scores exceeding 98% (EfficientNet-B0: 0.9893, ConvNeXt: 0.9920). ConvNeXt exhibited superior accuracy in distinguishing morphologically similar cells, attributed to its larger receptive fields and advanced architectural design, while EfficientNet-B0 demonstrated dramatic computational advantages with 134 FPS throughput and a compact model size of 18.3 MB six times smaller than ConvNeXt. Grad-CAM visualizations confirmed that both models focus on clinically relevant features including nuclear morphology and cytoplasmic characteristics, validated by low correlation with randomized models (average correlation <0.28) and significantly larger confidence drops during important region occlusion (6-18× greater than random occlusion). The findings establish evidence-based guidelines for model selection, ConvNeXt for high-precision diagnostic applications and EfficientNet-B0 for large-scale screening and edge deployment. This research contributes foundational evidence toward the development of transparent, reliable, and efficient computer-aided diagnosis systems, though prospective clinical validation on multi-institutional datasets remains an important direction for future work.
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