Monkeypox Diagnosis With Interpretable Deep Learning
As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL)-based disease prediction present...
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10198433/ |
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| author | Md. Manjurul Ahsan Md. Shahin Ali Md. Mehedi Hassan Tareque Abu Abdullah Kishor Datta Gupta Ulas Bagci Chetna Kaushal Naglaa F. Soliman |
| author_facet | Md. Manjurul Ahsan Md. Shahin Ali Md. Mehedi Hassan Tareque Abu Abdullah Kishor Datta Gupta Ulas Bagci Chetna Kaushal Naglaa F. Soliman |
| author_sort | Md. Manjurul Ahsan |
| collection | DOAJ |
| description | As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL)-based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavors that similarly showcase enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we used Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model’s predictions and identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas. |
| format | Article |
| id | doaj-art-fd4d051338f04b8fa035ecad8a86fa13 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fd4d051338f04b8fa035ecad8a86fa132025-08-20T03:43:55ZengIEEEIEEE Access2169-35362023-01-0111819658198010.1109/ACCESS.2023.330079310198433Monkeypox Diagnosis With Interpretable Deep LearningMd. Manjurul Ahsan0https://orcid.org/0000-0003-0900-7930Md. Shahin Ali1https://orcid.org/0000-0003-2564-8746Md. Mehedi Hassan2https://orcid.org/0000-0002-9890-0968Tareque Abu Abdullah3Kishor Datta Gupta4Ulas Bagci5Chetna Kaushal6https://orcid.org/0000-0002-3298-5583Naglaa F. Soliman7https://orcid.org/0000-0001-7322-1857Department of Radiology, Machine and Hybrid Intelligence Laboratory, Northwestern University, Chicago, IL, USADepartment of Biomedical Engineering, Islamic University, Kushtia, BangladeshComputer Science and Engineering Discipline, Khulna University, Khulna, BangladeshDepartment of Computer Science, Lamar University, Beaumont, TX, USADepartment of Computer and Information Science, Clark Atlanta University, Atlanta, GA, USADepartment of Radiology, BME, and ECE, Machine and Hybrid Intelligence Laboratory, Northwestern University, Chicago, IL, USAChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaAs the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL)-based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavors that similarly showcase enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we used Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model’s predictions and identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas.https://ieeexplore.ieee.org/document/10198433/Deep learningmonkeypoxdisease diagnosistransfer learningimage processingLIME |
| spellingShingle | Md. Manjurul Ahsan Md. Shahin Ali Md. Mehedi Hassan Tareque Abu Abdullah Kishor Datta Gupta Ulas Bagci Chetna Kaushal Naglaa F. Soliman Monkeypox Diagnosis With Interpretable Deep Learning IEEE Access Deep learning monkeypox disease diagnosis transfer learning image processing LIME |
| title | Monkeypox Diagnosis With Interpretable Deep Learning |
| title_full | Monkeypox Diagnosis With Interpretable Deep Learning |
| title_fullStr | Monkeypox Diagnosis With Interpretable Deep Learning |
| title_full_unstemmed | Monkeypox Diagnosis With Interpretable Deep Learning |
| title_short | Monkeypox Diagnosis With Interpretable Deep Learning |
| title_sort | monkeypox diagnosis with interpretable deep learning |
| topic | Deep learning monkeypox disease diagnosis transfer learning image processing LIME |
| url | https://ieeexplore.ieee.org/document/10198433/ |
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