Klasifikasi Subtipe Leukemia Limfoblastik Akut (LLA) pada Citra Mikroskopis Sel Darah Menggunakan Arsitektur EfficientNet-B3 dengan Dataset Seimbang - Agustina, Ni Putu Dina; Wijayakusuma, I Gusti Ngurah Lanang (Jurnal Locus Penelitian dan Pengabdian, 2025)
Acute Lymphoblastic Leukemia (ALL) is one of the most common types of blood cancer that affects children and requires fast and accurate diagnosis. This study proposes a classification model for subtypes of acute lymphoblastic leukemia (ALL) based on microscopic blood cell images using the EfficientNet-B3 architecture. With a transfer learning approach and a balanced dataset, the model achieves a testing accuracy of 97.50% and an average F1-Score of 0.97. Overall, the macro average and weighted average values show consistent results, with precision and recall of 0.98 and an F1-Score of 0.97. This indicates that the model excels not only in one or two classes but demonstrates uniform performance across all classes, making it a robust classification tool for automatic leukemia diagnosis applications.