Analysis of Blood Cell of Acute Lymphocytic Leukemia Using Different Image Classification Models

Abstract

Acute Lymphocytic Leukemia (ALL) is one of the most common and malignant tumors that is prevalent in both the young and old. However, it is most commonly found in very young children. Hence, early diagnosis and prognosis are important to improve the patient’s survival rate. With AI, improving screening, which is one of the methods of detection that is more sensitive and accurate will allow better analysis and will in turn assist medical professionals in their line of work. This research based on image classification will explore a deep learning-based model on convolutional neural networks (CNN), using four EfficientNet models including EfficientNetB0, EfficientNetB1, EfficientNetB2, and EfficietNetB3, to compare which of these models will produce the best results.