Enhancing Disease Classification with Deep Learning: a Two-Stage Optimization Approach for Monkeypox and Similar Skin Lesion Diseases.
Monkeypox (MPox) is an infectious disease caused by the monkeypox virus, presenting challenges in accurate identification due to its resemblance to other diseases. This study introduces a deep learning-based method to distinguish visually similar diseases, specifically MPox, chickenpox, and measles,...
| Published in: | Journal of Digital Imaging Vol. 37; no. 2; pp. 778 - 801 |
|---|---|
| Main Author: | |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2024
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177625992&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177625992 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177625992 177625992 177625992 10.1007/s10278-023-00941-7 177625992 ppf: 778 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Enhancing Disease Classification with Deep Learning: a Two-Stage Optimization Approach for Monkeypox and Similar Skin Lesion Diseases. aug: au: Savaş, Serkan affil: https://ror.org/01zhwwf82 Department of Computer Engineering, Kırıkkale University, 71450, Kırıkkale, Turkey sug: subj: Monkeypox Pathology Skin Neoplasms Pathology Monkeypox Classification Skin Neoplasms Classification Disease Attributes Deep Learning Human Chickenpox Measles Disease Outbreaks Neural Networks (Computer) Validation Studies Monkeypox Diagnosis Skin Neoplasms Diagnosis Sensitivity and Specificity Medical Informatics ab: Monkeypox (MPox) is an infectious disease caused by the monkeypox virus, presenting challenges in accurate identification due to its resemblance to other diseases. This study introduces a deep learning-based method to distinguish visually similar diseases, specifically MPox, chickenpox, and measles, addressing the 2022 global MPox outbreak. A two-stage optimization approach was presented in the study. By analyzing pre-trained deep neural networks including 71 models, this study optimizes accuracy through transfer learning, fine-tuning, and ensemble learning techniques. ConvNeXtBase, Large, and XLarge models were identified achieving 97.5% accuracy in the first stage. Afterwards, some selection criteria were followed for the models identified in the first stage for use in ensemble learning technique within the optimization approach. The top-performing ensemble model, EM3 (composed of RegNetX160, ResNetRS101, and ResNet101), attains an AUC of 0.9971 in the second stage. Evaluation on unseen data ensures model robustness and enhances the study's overall validity and reliability. The design and implementation of the study have been optimized to address the limitations identified in the literature. This approach offers a rapid and highly accurate decision support system for timely MPox diagnosis, reducing human error, manual processes, and enhancing clinic efficiency. It aids in early MPox detection, addresses diverse disease challenges, and informs imaging device software development. The study's broad implications support global health efforts and showcase artificial intelligence potential in medical informatics for disease identification and diagnosis. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|