Dynamic kernel generation through hybrid involution and convolution neural networks for leukemia and white blood cell classification.

Blood cancer diagnosis through microscopic image analysis is challenging due to subtle morphological differences between cell stages and subtypes. This study aims to develop a Hybrid Involutional-Convolutional Neural Network (HICNN) for automated leukemia detection and white blood cell morphology analysis, enabling precise classification of leukemia stages and leukocyte subtypes to improve diagnostic accuracy. The HICNN integrates involution layers, which dynamically generate spatially adaptive kernels for context-specific feature extraction, with convolutional layers that capture translation-invariant hierarchical patterns. This hybrid architecture leverages parallel processing within hybrid blocks to enhance discrimination of subtle cellular variations. The HICNN achieved exceptional performance with 99.5% accuracy on a leukemia staging dataset and 98.00% accuracy on a leukocyte subtyping dataset, surpassing state-of-the-art models. Model reliability was demonstrated through Brier scores of 0.0019 and 0.0069, with inter-class misclassifications below 2% and minimal overlap between class confidence scores. Training performance showed stable convergence within 50 epochs, with validation accuracy exceeding 99% by epoch 30. The HICNN advances automated hematological diagnostics by successfully addressing feature discrimination and model calibration challenges. The framework demonstrates robust performance in both cancer staging and cell subtype classification tasks, showing promise for reducing diagnostic ambiguity and improving early detection in leukemia and related hematological disorders.