Please use this identifier to cite or link to this item:
Title: Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network
Authors: Ansari, S
Navin, AH
Babazadeh Sangar, A
Vaez Gharamaleki, J
Danishvar, S
Keywords: type-II fuzzy deep network;leukemia;white blood cells;lymphocytes;monocytes
Issue Date: 24-Jan-2023
Publisher: MDPI
Citation: Ansari, S. et al. (2023) 'Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network', Electronics (Switzerland), 12 (5), 1116, pp. 1 - 15. doi: 10.3390/electronics12051116.
Abstract: Copyright © 2023 by the authors. A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.
Description: Data Availability Statement: The data related to this article are publicly available on the GitHub platform under the title Ansari acute leukemia images.
Other Identifiers: ORCID iDs: Amin Babazadeh Sangar; Sebelan Danishvar
Appears in Collections:Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons