Reza Goodarzi¹, Ali Jalali^1,*, Omid Hashemi Pour Tafreshi¹, Jalil Mazloum¹, Peyman Beygi²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 653-679, 2025, DOI:10.32604/cmc.2025.062803 - 09 June 2025

Abstract Acute lymphoblastic leukemia (ALL) is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis. One of the most prioritized tasks is the early and correct diagnosis of this malignancy; however, manual observation of the blood smear is very time-consuming and requires labor and expertise. Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging. Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2. This approach fuses deep and spatial features to… More >

Nilkanth Mukund Deshpande^1,2, Shilpa Gite^3,4,*, Biswajeet Pradhan^5,6, Abdullah Alamri⁷, Chang-Wook Lee^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704 - 30 December 2023

Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood… More > Graphic Abstract

Ghada Emam Atteia^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 361-376, 2023, DOI:10.32604/csse.2023.029597 - 16 August 2022

Abstract Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates… More >