The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer
Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper d...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Molecular Diversity Preservation International (MDPI)
2023
|
| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/36077/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/36077/2/FULL%20TEXT.pdf |
| _version_ | 1825715267523575808 |
|---|---|
| author | Tehseen Mazhar Inayatul Haq Allah Ditta Syed Agha Hassnain Mohsan Faisal Rehman Imran Zafar Jualang Azlan Gansau Lucky Poh Wah Goh |
| author_facet | Tehseen Mazhar Inayatul Haq Allah Ditta Syed Agha Hassnain Mohsan Faisal Rehman Imran Zafar Jualang Azlan Gansau Lucky Poh Wah Goh |
| author_sort | Tehseen Mazhar |
| collection | UMS |
| description | Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too. |
| first_indexed | 2024-03-06T03:24:18Z |
| format | Article |
| id | ums.eprints-36077 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:24:18Z |
| publishDate | 2023 |
| publisher | Molecular Diversity Preservation International (MDPI) |
| record_format | dspace |
| spelling | ums.eprints-360772023-07-20T01:35:59Z https://eprints.ums.edu.my/id/eprint/36077/ The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer Tehseen Mazhar Inayatul Haq Allah Ditta Syed Agha Hassnain Mohsan Faisal Rehman Imran Zafar Jualang Azlan Gansau Lucky Poh Wah Goh RC254-282 Neoplasms. Tumors. Oncology Including cancer and carcinogens TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too. Molecular Diversity Preservation International (MDPI) 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/36077/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/36077/2/FULL%20TEXT.pdf Tehseen Mazhar and Inayatul Haq and Allah Ditta and Syed Agha Hassnain Mohsan and Faisal Rehman and Imran Zafar and Jualang Azlan Gansau and Lucky Poh Wah Goh (2023) The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer. Healthcare, 11. pp. 1-22. . https://doi.org/10.3390/healthcare11030415 |
| spellingShingle | RC254-282 Neoplasms. Tumors. Oncology Including cancer and carcinogens TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Tehseen Mazhar Inayatul Haq Allah Ditta Syed Agha Hassnain Mohsan Faisal Rehman Imran Zafar Jualang Azlan Gansau Lucky Poh Wah Goh The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer |
| title | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer |
| title_full | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer |
| title_fullStr | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer |
| title_full_unstemmed | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer |
| title_short | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer |
| title_sort | role of machine learning and deep learning approaches for the detection of skin cancer |
| topic | RC254-282 Neoplasms. Tumors. Oncology Including cancer and carcinogens TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television |
| url | https://eprints.ums.edu.my/id/eprint/36077/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/36077/2/FULL%20TEXT.pdf |
| work_keys_str_mv | AT tehseenmazhar theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT inayatulhaq theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT allahditta theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT syedaghahassnainmohsan theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT faisalrehman theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT imranzafar theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT jualangazlangansau theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT luckypohwahgoh theroleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT tehseenmazhar roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT inayatulhaq roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT allahditta roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT syedaghahassnainmohsan roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT faisalrehman roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT imranzafar roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT jualangazlangansau roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer AT luckypohwahgoh roleofmachinelearninganddeeplearningapproachesforthedetectionofskincancer |