Deep Learning Techniques in Acute Lymphoblastic Leukemia

Abstract

Acute Lymphoblastic Leukemia (ALL) is a serious hematologic malignancy that requires prompt and accurate diagnosis for effective treatment. Traditional diagnostic approaches, such as microscopic inspection and flow cytometry, while reliable, are labor intensive and time-consuming. Deep learning (DL), particularly Convolutional Neural Networks (CNNs), has revolutionized ALL diagnosis by automatically extracting features from medical images with an accuracy comparable to experienced hematologists. This automation enhances diagnostic precision and significantly reduces diagnosis time, ultimately improving clinical decision-making and patient outcomes. This study uses deep learning through rigorous bibliometric analysis to examine research productivity indicators related to the classification of Acute Lymphoblastic Leukemia. The dataset comprises 152 publications on ALL classifications using deep learning from 2009 to 2023. The analysis is conducted using the R Bibliometrix library. These publications, authored by 654 researchers from 38 countries, are distributed across 99 sources, including journals and books. Collectively, the corpus has received 1,770 citations, with an average of 11.8 citations per document. The most-cited paper, Leukemia Diagnosis in Blood Slides Using Transfer Learning in CNNs and SVM for Classification by Luis H.S. Vogado et al., has been cited 143 times. In terms of global scientific output, India leads with 26 publications (17.3%), followed by China with 22 (14.7%) and the United States with 16 (10.7%). Keywords: bibliometric analysis; R Bibliometrix; Acute Lymphoblastic Leukemia; Deep Learning; Web of Science.