JoWUA

Abstract

Early and accurate diagnosis of Acute Lymphoblastic Leukemia (ALL) is essential to treat it successfully and improve patient outcomes. In this paper, a modified VGG-based hybrid architecture that combines an adapted K-means segmentation algorithm is proposed to classify leukocytes in blood smear images using this framework, and the researcher is able to classify blood cells based on the similarities between them. The segmentation step transforms images to the HSV color space and dynamically recalculates cluster centers using both color and morphological constraints, which ensure accurate leukocyte isolation in situations of overlapping cells and variability of staining. The fragmented areas are then subjected to a hybrid VGG network with residual skip-connection and attention modules to allow the extraction of discriminative features of the nuclear and cytoplasmic structures robustly. The framework was tested on 260 high-resolution images in the ALL-IDB dataset and the performance measured by metric values of accuracy, precision, recall, F1-score, specificity, and the Matthews Correlation Coefficient (MCC). The model achieved 98.5% accuracy, 96.5% precision, 97.0% recall, 98.5% F1-score, 97.9% specificity, and an MCC of 0.96, which is much higher than those of the state-of-the-art models ResNet-50, DenseNet121, and DDRNet. The results of cross-validation revealed low variance (±1.2%), indicating strong generalization across folds. Also, the mean processing time of 16.5 ms per image indicates that it can be used in real time. These findings indicate that advanced segmentation, hierarchical feature learning, and attention can be associated not only with better classification results but also with adequate reproducibility and reliability, and can be considered a clinically applicable tool for automated real-time detection of ALL in blood smear analysis.