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 (asymmetric)
characteristic of ALL. By integrating insights from generative modeling techniques,
the study explores how asymmetric distortions in cellular structures can serve as key markers
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 outperforms
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.