SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis
Accurate diagnosis of skin diseases remains a significant challenge due to the inherent limitations of traditional visual and manual examination methods. These conventional approaches, while essential to dermatological practice, are prone to misdiagnoses and delays in treatment, particularly for con...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000954 |
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| author | Amina Aboulmira Mohamed Lachgar Hamid Hrimech Aboudramane Camara Charafeddine Elbahja Amine Elmansouri Yassine Hassini |
| author_facet | Amina Aboulmira Mohamed Lachgar Hamid Hrimech Aboudramane Camara Charafeddine Elbahja Amine Elmansouri Yassine Hassini |
| author_sort | Amina Aboulmira |
| collection | DOAJ |
| description | Accurate diagnosis of skin diseases remains a significant challenge due to the inherent limitations of traditional visual and manual examination methods. These conventional approaches, while essential to dermatological practice, are prone to misdiagnoses and delays in treatment, particularly for conditions like skin cancer. To address these gaps, this paper presents the SkinHealth App, an innovative AI-driven solution that enhances the accuracy and efficiency of skin disease diagnosis. The app integrates a robust ensemble learning model, combining the strengths of EfficientNetB1 and EfficientNetB5 architectures. This ensemble model improves disease classification performance through advanced image processing techniques such as noise reduction and data augmentation. The key contributions of this work include the development of a scalable and secure server-side structure that ensures the safe handling of patient data and efficient processing of diagnostic queries. Experimental results on the HAM10000 dataset demonstrate the model's superior performance, achieving an accuracy of 93%, along with high precision and recall scores, thereby reducing false positives and false negatives. These outcomes clearly establish the app's potential to enhance dermatological diagnosis by providing timely and accurate disease identification. Ultimately, this work bridges the gap between traditional diagnostic methods and modern AI-driven technology, offering a transformative tool for improving patient care in dermatology. |
| format | Article |
| id | doaj-art-e986917a54aa4195b8a7e8f23f56f646 |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-e986917a54aa4195b8a7e8f23f56f6462024-12-19T11:03:16ZengElsevierSystems and Soft Computing2772-94192024-12-016200166SkinHealthMate app: An AI-powered digital platform for skin disease diagnosisAmina Aboulmira0Mohamed Lachgar1Hamid Hrimech2Aboudramane Camara3Charafeddine Elbahja4Amine Elmansouri5Yassine Hassini6LAMSAD Laboratory, ENSA, Hassan 1er University, Berrechid, Morocco; Corresponding author.L2IS Laboratory, Faculty of Science and Technology, University Cadi Ayyad, Marrakech, Morocco; Higher Normal School, Department of Computer Science, University Cadi Ayyad, Marrakech, Morocco; LTI Laboratory, ENSA, Chouaib Doukkali University, MoroccoLAMSAD Laboratory, ENSA, Hassan 1er University, Berrechid, MoroccoLTI Laboratory, ENSA, Chouaib Doukkali University, Morocco; IITE, ENSA, Chouaib Doukkali University, MoroccoLTI Laboratory, ENSA, Chouaib Doukkali University, Morocco; IITE, ENSA, Chouaib Doukkali University, MoroccoIITE, ENSA, Chouaib Doukkali University, MoroccoIITE, ENSA, Chouaib Doukkali University, MoroccoAccurate diagnosis of skin diseases remains a significant challenge due to the inherent limitations of traditional visual and manual examination methods. These conventional approaches, while essential to dermatological practice, are prone to misdiagnoses and delays in treatment, particularly for conditions like skin cancer. To address these gaps, this paper presents the SkinHealth App, an innovative AI-driven solution that enhances the accuracy and efficiency of skin disease diagnosis. The app integrates a robust ensemble learning model, combining the strengths of EfficientNetB1 and EfficientNetB5 architectures. This ensemble model improves disease classification performance through advanced image processing techniques such as noise reduction and data augmentation. The key contributions of this work include the development of a scalable and secure server-side structure that ensures the safe handling of patient data and efficient processing of diagnostic queries. Experimental results on the HAM10000 dataset demonstrate the model's superior performance, achieving an accuracy of 93%, along with high precision and recall scores, thereby reducing false positives and false negatives. These outcomes clearly establish the app's potential to enhance dermatological diagnosis by providing timely and accurate disease identification. Ultimately, this work bridges the gap between traditional diagnostic methods and modern AI-driven technology, offering a transformative tool for improving patient care in dermatology.http://www.sciencedirect.com/science/article/pii/S2772941924000954Artificial intelligenceDermatology ensemble learningSkin disease classificationDigital health platforms |
| spellingShingle | Amina Aboulmira Mohamed Lachgar Hamid Hrimech Aboudramane Camara Charafeddine Elbahja Amine Elmansouri Yassine Hassini SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis Systems and Soft Computing Artificial intelligence Dermatology ensemble learning Skin disease classification Digital health platforms |
| title | SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis |
| title_full | SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis |
| title_fullStr | SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis |
| title_full_unstemmed | SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis |
| title_short | SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis |
| title_sort | skinhealthmate app an ai powered digital platform for skin disease diagnosis |
| topic | Artificial intelligence Dermatology ensemble learning Skin disease classification Digital health platforms |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000954 |
| work_keys_str_mv | AT aminaaboulmira skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis AT mohamedlachgar skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis AT hamidhrimech skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis AT aboudramanecamara skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis AT charafeddineelbahja skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis AT amineelmansouri skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis AT yassinehassini skinhealthmateappanaipowereddigitalplatformforskindiseasediagnosis |