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Kassem MA, Hosny KM, Damaševičius R, Eltoukhy MM. Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review. Diagnostics (Basel) 2021; 11:1390. [PMID: 34441324 PMCID: PMC8391467 DOI: 10.3390/diagnostics11081390] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/04/2022] Open
Abstract
Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.
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Affiliation(s)
- Mohamed A. Kassem
- Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kaferelshiekh University, Kaferelshiekh 33511, Egypt;
| | - Khalid M. Hosny
- Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
| | - Mohamed Meselhy Eltoukhy
- Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt;
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Maglogiannis I, Kontogianni G, Papadodima O, Karanikas H, Billiris A, Chatziioannou A. An Integrated Platform for Skin Cancer Heterogenous and Multilayered Data Management. J Med Syst 2021; 45:10. [PMID: 33404959 DOI: 10.1007/s10916-020-01679-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 11/23/2020] [Indexed: 01/22/2023]
Abstract
Electronic health record (EHR) systems improve health care services by allowing the combination of health data with clinical decision support features and clinical image analyses. This study presents a modular and distributed platform that is able to integrate and accommodate heterogeneous, multidimensional (omics, histological images and clinical) data for the multi-angled portrayal and management of skin cancer patients. The proposed design offers a layered analytical framework as an expansion of current EHR systems, which can integrate high-volume molecular -omics data, imaging data, as well as relevant clinical observations. We present a case study in the field of dermatology, where we attempt to combine the multilayered information for the early detection and characterization of melanoma. The specific architecture aspires to lower the barrier for the introduction of personalized therapeutic approaches, towards precision medicine. The paper describes the technical issues of implementation, along with an initial evaluation of the system and discussion.
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Affiliation(s)
- Ilias Maglogiannis
- Department of Digital Systems, University of Piraeus, 126 Grigoriou Lambraki, 18534, Piraeus, Greece.
| | - Georgia Kontogianni
- Department of Digital Systems, University of Piraeus, 126 Grigoriou Lambraki, 18534, Piraeus, Greece
- National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, 11635, Athens, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
| | - Olga Papadodima
- National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, 11635, Athens, Greece
| | | | | | - Aristotelis Chatziioannou
- National Hellenic Research Foundation, 48 Vassileos Constantinou Ave, 11635, Athens, Greece
- Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou, 11527, Athens, Greece
- e-NIOS Applications Private Company, 17671, Kallithea, Greece
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Wang L, Wang Y, Chang Q. Feature selection methods for big data bioinformatics: A survey from the search perspective. Methods 2016; 111:21-31. [PMID: 27592382 DOI: 10.1016/j.ymeth.2016.08.014] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Revised: 08/25/2016] [Accepted: 08/30/2016] [Indexed: 11/26/2022] Open
Abstract
This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures.
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Affiliation(s)
- Lipo Wang
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
| | - Yaoli Wang
- College of Information Engineering, Taiyuan University of Technology, Taiyuan, China.
| | - Qing Chang
- College of Information Engineering, Taiyuan University of Technology, Taiyuan, China.
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Akay M, Fotiadis DI, Nikita KS, Williams RW. Guest Editorial: Biomedical Informatics in Clinical Environments. IEEE J Biomed Health Inform 2015; 19:149-50. [DOI: 10.1109/jbhi.2014.2382771] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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