Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization
<p dir="ltr">Diabetes mellitus (DM) can cause chronic foot issues and severe infections, including Diabetic Foot Ulcers (DFUs) that heal slowly due to insufficient blood flow. A recurrence of these ulcers can lead to 84% of lower limb amputations and even cause death. High-risk diabe...
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513543705985024 |
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| author | Rusab Sarmun (17632269) |
| author2 | Muhammad E. H. Chowdhury (14150526) M. Murugappan (18842221) Ahmed Aqel (21767678) Maymouna Ezzuddin (21767681) Syed Mahfuzur Rahman (21767684) Amith Khandakar (14151981) Sanzida Akter (21767687) Rashad Alfkey (17093008) Anwarul Hasan (1332066) |
| author2_role | author author author author author author author author author |
| author_facet | Rusab Sarmun (17632269) Muhammad E. H. Chowdhury (14150526) M. Murugappan (18842221) Ahmed Aqel (21767678) Maymouna Ezzuddin (21767681) Syed Mahfuzur Rahman (21767684) Amith Khandakar (14151981) Sanzida Akter (21767687) Rashad Alfkey (17093008) Anwarul Hasan (1332066) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rusab Sarmun (17632269) Muhammad E. H. Chowdhury (14150526) M. Murugappan (18842221) Ahmed Aqel (21767678) Maymouna Ezzuddin (21767681) Syed Mahfuzur Rahman (21767684) Amith Khandakar (14151981) Sanzida Akter (21767687) Rashad Alfkey (17093008) Anwarul Hasan (1332066) |
| dc.date.none.fl_str_mv | 2024-04-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s12559-024-10267-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Diabetic_Foot_Ulcer_Detection_Combining_Deep_Learning_Models_for_Improved_Localization/29625089 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Diabetic foot ulcer (DFU) Weighted bounding box fusion (WBF) Machine learning Deep learning Diabetic Foot Ulcer Challenge 2020 (DFUC2020) |
| dc.title.none.fl_str_mv | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Diabetes mellitus (DM) can cause chronic foot issues and severe infections, including Diabetic Foot Ulcers (DFUs) that heal slowly due to insufficient blood flow. A recurrence of these ulcers can lead to 84% of lower limb amputations and even cause death. High-risk diabetes patients require expensive medications, regular check-ups, and proper personal hygiene to prevent DFUs, which affect 15–25% of diabetics. Accurate diagnosis, appropriate care, and prompt response can prevent amputations and fatalities through early and reliable DFU detection from image analysis. We propose a comprehensive deep learning-based system for detecting DFUs from patients’ feet images by reliably localizing ulcer points. Our method utilizes innovative model ensemble techniques—non-maximum suppression (NMS), Soft-NMS, and weighted bounding box fusion (WBF)—to combine predictions from state-of-the-art object detection models. The performances of diverse cutting-edge model architectures used in this study complement each other, leading to more generalized and improved results when combined in an ensemble. Our WBF-based approach combining YOLOv8m and FRCNN-ResNet101 achieves a mean average precision (mAP) score of 86.4% at the IoU threshold of 0.5 on the DFUC2020 dataset, significantly outperforming the former benchmark by 12.4%. We also perform external validation on the IEEE DataPort Diabetic Foot dataset which has demonstrated robust and reliable model performance on the qualitative analysis. In conclusion, our study effectively developed an innovative diabetic foot ulcer (DFU) detection system using an ensemble model of deep neural networks (DNNs). This AI-driven tool serves as an initial screening aid for medical professionals, augmenting the diagnostic process by enhancing sensitivity to potential DFU cases. While recognizing the presence of false positives, our research contributes to improving patient care through the integration of human medical expertise with AI-based solutions in DFU management.</p><h2>Other Information</h2><p dir="ltr">Published in: Cognitive Computation<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s12559-024-10267-3" target="_blank">https://dx.doi.org/10.1007/s12559-024-10267-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c135f92cf31fcb12e7d6837ea5cc8cdf |
| identifier_str_mv | 10.1007/s12559-024-10267-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29625089 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved LocalizationRusab Sarmun (17632269)Muhammad E. H. Chowdhury (14150526)M. Murugappan (18842221)Ahmed Aqel (21767678)Maymouna Ezzuddin (21767681)Syed Mahfuzur Rahman (21767684)Amith Khandakar (14151981)Sanzida Akter (21767687)Rashad Alfkey (17093008)Anwarul Hasan (1332066)Biomedical and clinical sciencesClinical sciencesMedical biochemistry and metabolomicsEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningDiabetic foot ulcer (DFU)Weighted bounding box fusion (WBF)Machine learningDeep learningDiabetic Foot Ulcer Challenge 2020 (DFUC2020)<p dir="ltr">Diabetes mellitus (DM) can cause chronic foot issues and severe infections, including Diabetic Foot Ulcers (DFUs) that heal slowly due to insufficient blood flow. A recurrence of these ulcers can lead to 84% of lower limb amputations and even cause death. High-risk diabetes patients require expensive medications, regular check-ups, and proper personal hygiene to prevent DFUs, which affect 15–25% of diabetics. Accurate diagnosis, appropriate care, and prompt response can prevent amputations and fatalities through early and reliable DFU detection from image analysis. We propose a comprehensive deep learning-based system for detecting DFUs from patients’ feet images by reliably localizing ulcer points. Our method utilizes innovative model ensemble techniques—non-maximum suppression (NMS), Soft-NMS, and weighted bounding box fusion (WBF)—to combine predictions from state-of-the-art object detection models. The performances of diverse cutting-edge model architectures used in this study complement each other, leading to more generalized and improved results when combined in an ensemble. Our WBF-based approach combining YOLOv8m and FRCNN-ResNet101 achieves a mean average precision (mAP) score of 86.4% at the IoU threshold of 0.5 on the DFUC2020 dataset, significantly outperforming the former benchmark by 12.4%. We also perform external validation on the IEEE DataPort Diabetic Foot dataset which has demonstrated robust and reliable model performance on the qualitative analysis. In conclusion, our study effectively developed an innovative diabetic foot ulcer (DFU) detection system using an ensemble model of deep neural networks (DNNs). This AI-driven tool serves as an initial screening aid for medical professionals, augmenting the diagnostic process by enhancing sensitivity to potential DFU cases. While recognizing the presence of false positives, our research contributes to improving patient care through the integration of human medical expertise with AI-based solutions in DFU management.</p><h2>Other Information</h2><p dir="ltr">Published in: Cognitive Computation<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s12559-024-10267-3" target="_blank">https://dx.doi.org/10.1007/s12559-024-10267-3</a></p>2024-04-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s12559-024-10267-3https://figshare.com/articles/journal_contribution/Diabetic_Foot_Ulcer_Detection_Combining_Deep_Learning_Models_for_Improved_Localization/29625089CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296250892024-04-01T00:00:00Z |
| spellingShingle | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization Rusab Sarmun (17632269) Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Diabetic foot ulcer (DFU) Weighted bounding box fusion (WBF) Machine learning Deep learning Diabetic Foot Ulcer Challenge 2020 (DFUC2020) |
| status_str | publishedVersion |
| title | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| title_full | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| title_fullStr | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| title_full_unstemmed | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| title_short | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| title_sort | Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization |
| topic | Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Diabetic foot ulcer (DFU) Weighted bounding box fusion (WBF) Machine learning Deep learning Diabetic Foot Ulcer Challenge 2020 (DFUC2020) |