A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis.
Authors
Affiliations (6)
Affiliations (6)
- Departamento de Ciencias Exactas y Tecnología, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno, Jalisco, Mexico. Electronic address: [email protected].
- International Islamic University of Islamabad, Islamabad, Pakistan.
- Departamento de Ciencias Exactas y Tecnología, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno, Jalisco, Mexico. Electronic address: [email protected].
- Departamento de Oftalmología, Hospital Civil de Guadalajara, Fray Antonio Alcalde, Guadalajara, Jalisco 44280, Mexico.
- Departamento de Ciencias de la Tierra y de la Vida, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno, Jalisco, Mexico.
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain. Electronic address: [email protected].
Abstract
Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid proliferation of immature white blood cells in the bone marrow. Early and accurate diagnosis is essential for improving clinical outcomes; however, distinguishing between lymphocytes and lymphoblasts poses significant challenges owing to their subtle morphological similarities. Traditional manual diagnostic methods, which rely on expert evaluations, are inherently time-consuming and subject to human error. In recent years, machine learning and deep learning approaches have emerged as promising tools for automating and enhancing diagnostic processes. This review systematically examines state-of-the-art traditional and deep learning techniques applied for ALL detection and classification. We provide a comprehensive analysis of various methodologies, including supervised machine learning algorithms and advanced deep learning architectures, with a focus on critical stages such as image preprocessing, feature extraction, and blast cell quantification. Furthermore, we discuss the performance metrics and accuracy benchmarks, highlighting the potential of these techniques to match or exceed human diagnostic capabilities. The review concludes with a discussion of the current challenges, recent developments, and future directions in the application of artificial intelligence for ALL diagnosis, underscoring the need for continued innovation to meet emerging clinical demands.