A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database  

作　　者：Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Abdullah Alamri Chang-Wook Lee 

机构地区：[1]Department of Electronics&Telecommunication,Lavale,Symbiosis Institute of Technology,Symbiosis International(Deemed University),Pune,Maharashtra,412115,India [2]Electronics&Telecommunication,Dr.Vithalrao Vikhe Patil College of Engineering,Ahmednagar,Maharashtra,414111,India [3]Artificial Intelligence and Machine Learning Department,Symbiosis Institute of Technology,Symbiosis International(Deemed)University,Pune,412115,India [4]Symbiosis Centre of Applied AI(SCAAI),Symbiosis International(Deemed)University,Pune,412115,India [5]Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS),School of Civil and Environmental Engineering,Faculty of Engineering&IT,University of Technology Sydney,Sydney,Australia [6]Earth Observation Centre,Institute of Climate Change,Universiti Kebangsaan Malaysia,Bangi,Selangor,43600,Malaysia [7]Department of Geology&Geophysics,College of Science,King Saud University,P.O.Box 2455,Riyadh,11451,Saudi Arabia [8]Department of Science Education,Kangwon National University,Chuncheon-si,24341,Korea

出　　处：《Computer Modeling in Engineering & Sciences》2024年第4期593-631,共39页工程与科学中的计算机建模（英文）

基　　金：supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS),the University of Technology Sydney,the Ministry of Education of the Republic of Korea,and the National Research Foundation of Korea (NRF-2023R1A2C1007742);in part by the Researchers Supporting Project Number RSP-2023/14,King Saud University。

摘　　要：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 leukemiainfected 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 cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class cl

关 键 词：Leukemia diagnosis deep learning machine learning random forest XGBoost 

分 类 号：R733.7[医药卫生—肿瘤]