Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis
Abstract Problem Skin lesions are the major indicator for diagnosing different skin diseases, which are caused by the abnormal growth of skin cells. Skin cancer, one of the most fatal types of cancer in the world, relies on the proper diagnosis of skin lesions and other relevant disease indicators f...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
|
| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-024-06448-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850134786224422912 |
|---|---|
| author | Abida Noaman Reyaz Ahmad Muhammad Farhan Khan Abdul Salam Mohammed Muhammad Farooq Khan Muhammad Adnan |
| author_facet | Abida Noaman Reyaz Ahmad Muhammad Farhan Khan Abdul Salam Mohammed Muhammad Farooq Khan Muhammad Adnan |
| author_sort | Abida Noaman |
| collection | DOAJ |
| description | Abstract Problem Skin lesions are the major indicator for diagnosing different skin diseases, which are caused by the abnormal growth of skin cells. Skin cancer, one of the most fatal types of cancer in the world, relies on the proper diagnosis of skin lesions and other relevant disease indicators for early detection, which can enhance treatment and increase life expectancy by up to 92%. Diagnosing skin lesions accurately is challenging, even for experienced practitioners. Challenge Artificial Intelligence (AI) techniques based on deep learning have proved promising in the field of medical image processing and diagnosis. However, there is a lack of comprehensive medical image datasets. This paper will present a skin lesion classification model that employs a transfer learning approach based on Alex Net and classifies eight different skin diseases. This proposed model was trained, validated, and tested using ISIC 2019 challenge data with a very abnormal class distribution. This variation in class size has been overcome using data augmentation and resizing in the preprocessing phase. Preprocessing is performed in such a way that no colour detail or rich identification features will be lost from the images. Results The proposed model achieved a testing accuracy of 90.9%, with a precision of 90.8%, a sensitivity of 90.9%, and an F1-Score of 90.8%. The values for precision and sensitivity are among the highest reported, specifically for models using the ISIC 2019 dataset. |
| format | Article |
| id | doaj-art-c30d8d9c9882431fb628b9bb3b396079 |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-c30d8d9c9882431fb628b9bb3b3960792025-08-20T02:31:38ZengSpringerDiscover Applied Sciences3004-92612024-12-017111810.1007/s42452-024-06448-2Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosisAbida Noaman0Reyaz Ahmad1Muhammad Farhan Khan2Abdul Salam Mohammed3Muhammad Farooq4Khan Muhammad Adnan5Riphah School of Computing and Innovation, Riphah International University, Lahore CampusDepartment of General Education, Skyline University College, University City SharjahDepartment of Forensic Sciences, University of Health SciencesDepartment of General Education, Skyline University College, University City SharjahDepartment of Mathematics and Statistics, College of Arts and Sciences, Abu Dhabi UniversityDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon UniversityAbstract Problem Skin lesions are the major indicator for diagnosing different skin diseases, which are caused by the abnormal growth of skin cells. Skin cancer, one of the most fatal types of cancer in the world, relies on the proper diagnosis of skin lesions and other relevant disease indicators for early detection, which can enhance treatment and increase life expectancy by up to 92%. Diagnosing skin lesions accurately is challenging, even for experienced practitioners. Challenge Artificial Intelligence (AI) techniques based on deep learning have proved promising in the field of medical image processing and diagnosis. However, there is a lack of comprehensive medical image datasets. This paper will present a skin lesion classification model that employs a transfer learning approach based on Alex Net and classifies eight different skin diseases. This proposed model was trained, validated, and tested using ISIC 2019 challenge data with a very abnormal class distribution. This variation in class size has been overcome using data augmentation and resizing in the preprocessing phase. Preprocessing is performed in such a way that no colour detail or rich identification features will be lost from the images. Results The proposed model achieved a testing accuracy of 90.9%, with a precision of 90.8%, a sensitivity of 90.9%, and an F1-Score of 90.8%. The values for precision and sensitivity are among the highest reported, specifically for models using the ISIC 2019 dataset.https://doi.org/10.1007/s42452-024-06448-2Artificial intelligenceMachine learningTransfer learningSkin disease classification |
| spellingShingle | Abida Noaman Reyaz Ahmad Muhammad Farhan Khan Abdul Salam Mohammed Muhammad Farooq Khan Muhammad Adnan Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis Discover Applied Sciences Artificial intelligence Machine learning Transfer learning Skin disease classification |
| title | Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis |
| title_full | Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis |
| title_fullStr | Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis |
| title_full_unstemmed | Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis |
| title_short | Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis |
| title_sort | beyond binary multi class skin lesion classification with alexnet transfer learning towards enhanced dermatological diagnosis |
| topic | Artificial intelligence Machine learning Transfer learning Skin disease classification |
| url | https://doi.org/10.1007/s42452-024-06448-2 |
| work_keys_str_mv | AT abidanoaman beyondbinarymulticlassskinlesionclassificationwithalexnettransferlearningtowardsenhanceddermatologicaldiagnosis AT reyazahmad beyondbinarymulticlassskinlesionclassificationwithalexnettransferlearningtowardsenhanceddermatologicaldiagnosis AT muhammadfarhankhan beyondbinarymulticlassskinlesionclassificationwithalexnettransferlearningtowardsenhanceddermatologicaldiagnosis AT abdulsalammohammed beyondbinarymulticlassskinlesionclassificationwithalexnettransferlearningtowardsenhanceddermatologicaldiagnosis AT muhammadfarooq beyondbinarymulticlassskinlesionclassificationwithalexnettransferlearningtowardsenhanceddermatologicaldiagnosis AT khanmuhammadadnan beyondbinarymulticlassskinlesionclassificationwithalexnettransferlearningtowardsenhanceddermatologicaldiagnosis |