A Robust EfficientNetV2-S Classifier for Predicting Acute Lymphoblastic Leukemia Based on Cross Validation

This research addresses the challenges of early detection of Acute Lymphoblastic
Leukemia (ALL), a life-threatening blood cancer particularly prevalent in children. Manual
diagnosis of ALL is often error-prone, time-consuming, and reliant on expert interpretation,
leading to delays in treatment. This study proposes an automated binary classification
model based on the EfficientNetV2-S architecture to overcome these limitations, enhanced
with 5-fold cross-validation (5KCV) for robust performance. A novel aspect of this research
lies in leveraging the symmetry concepts of symmetric and asymmetric patterns within the
microscopic imagery of white blood cells. Symmetry plays a critical role in distinguishing
typical cellular structures (symmetric) from the abnormal morphological patterns (asym-
metric) characteristic of ALL. By integrating insights from generative modeling techniques,
the study explores how asymmetric distortions in cellular structures can serve as key mark-
ers for disease classification. The EfficientNetV2-S model was trained and validated using
the normalized C-NMC_Leukemia dataset, achieving exceptional metrics: 97.34% accuracy,
recall, precision, specificity, and F1-score. Comparative analysis showed the model outper-
forms recent classifiers, making it highly effective for distinguishing abnormal white blood
cells. This approach accelerates diagnosis, reduces costs, and improves patient outcomes,
offering a transformative tool for early ALL detection and treatment planning.