Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT

(1) Background: Acute Myeloid Leukemia (AML) is a complex hematologic malignancy where accurate subtype classification is crucial for targeted treatment and improved patient outcomes. Automated AML subtype detection is especially important for underrepresented subtypes to ensure equitable diagnostics; (2) Methods: This study explores the potential of ConvNeXt, an advanced convolutional neural network architecture, for classifying high-resolution peripheral blood smear images into AML subtypes. A deep learning pipeline was developed, integrating Stochastic Weight Averaging (SWA) for model stability, Mixup data augmentation to enhance generalization, and Grad-CAM for model interpretability, ensuring biologically meaningful feature visualization. Various models, including ResNet50 and Vision Transformers, were benchmarked for comparative performance analysis; (3) Results: ConvNeXt outperformed ResNet50, achieving a classification accuracy of 95% compared to 91% for ResNet50 and 81% for transformer-based models (Vision Transformers). Grad-CAM visualizations provided biologically interpretable heatmaps, enhancing trust in computational predictions and bridging the gap between AI-driven diagnostics and clinical decision-making. Ablation studies highlighted the contributions of data augmentation, optimizer selection, and hyperparameter tuning, demonstrating the robustness and adaptability of the model; (4) Conclusions: This study advances AI’s role in hematopathology by combining high classification performance, explainability, and scalability. ConvNeXt offers a robust, interpretable, and scalable solution for AML subtype classification, improving diagnostic precision and supporting clinical decision-making. These results underscore the potential for AI-driven advancements in equitable and efficient AML diagnostics.