A Comprehensive Joint Learning System to Detect Skin Cancer
Skin, the body’s biggest organ and a barrier against heat, light, damage, and infection can be affected by many diseases. However, a correct diagnosis can lead to proper treatment. Skin diseases must be identified early to reduce skin lesion growth and spread. The medical field has a sign...
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| Format: | Article |
| Language: | English |
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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/10189856/ |
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| _version_ | 1849233880455839744 |
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| author | Lubna Riaz Hafiz Muhammad Qadir Ghulam Ali Mubashir Ali Muhammad Ahsan Raza Anca D. Jurcut Jehad Ali |
| author_facet | Lubna Riaz Hafiz Muhammad Qadir Ghulam Ali Mubashir Ali Muhammad Ahsan Raza Anca D. Jurcut Jehad Ali |
| author_sort | Lubna Riaz |
| collection | DOAJ |
| description | Skin, the body’s biggest organ and a barrier against heat, light, damage, and infection can be affected by many diseases. However, a correct diagnosis can lead to proper treatment. Skin diseases must be identified early to reduce skin lesion growth and spread. The medical field has a significant dependency on Information Technology and in this era, there is a need for a mechanism that can detect skin diseases at an early stage with higher accuracy capable of working with rapidly growing data. This research offers a joint learning system using Convolutional Neural Networks (CNN) and Local Binary Pattern (LBP) followed by its concatenation of all the extracted features through CNN and LBP architecture. The proposed system is trained and tested using the widely used publicly accessible dataset for skin cancer detection to solve multiclass skin disease issues. Furthermore, a comparison of results is developed between the architectures and their fusion. The demonstration of the results shows the robustness of the fusion architecture with an accuracy of 98.60% and a validation accuracy of 97.32%. Comparative results are also included in this research for better analysis. |
| format | Article |
| id | doaj-art-01b9347c320f4e3797c39b764108b025 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-01b9347c320f4e3797c39b764108b0252025-08-20T04:03:21ZengIEEEIEEE Access2169-35362023-01-0111794347944410.1109/ACCESS.2023.329764410189856A Comprehensive Joint Learning System to Detect Skin CancerLubna Riaz0Hafiz Muhammad Qadir1Ghulam Ali2https://orcid.org/0000-0002-0726-2738Mubashir Ali3Muhammad Ahsan Raza4Anca D. Jurcut5https://orcid.org/0000-0002-2705-1823Jehad Ali6https://orcid.org/0000-0002-0589-7924Department of Computer Science, University of Okara, Okara, PakistanDepartment of Software Engineering, Lahore Garrison University, Lahore, PakistanDepartment of Computer Science, University of Okara, Okara, PakistanCenter for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Information Sciences, University of Education, Multan Campus, Lahore, PakistanSchool of Computer Science, University College Dublin, Dublin 4, IrelandDepartment of AI Convergence Network, Ajou University, Suwon, South KoreaSkin, the body’s biggest organ and a barrier against heat, light, damage, and infection can be affected by many diseases. However, a correct diagnosis can lead to proper treatment. Skin diseases must be identified early to reduce skin lesion growth and spread. The medical field has a significant dependency on Information Technology and in this era, there is a need for a mechanism that can detect skin diseases at an early stage with higher accuracy capable of working with rapidly growing data. This research offers a joint learning system using Convolutional Neural Networks (CNN) and Local Binary Pattern (LBP) followed by its concatenation of all the extracted features through CNN and LBP architecture. The proposed system is trained and tested using the widely used publicly accessible dataset for skin cancer detection to solve multiclass skin disease issues. Furthermore, a comparison of results is developed between the architectures and their fusion. The demonstration of the results shows the robustness of the fusion architecture with an accuracy of 98.60% and a validation accuracy of 97.32%. Comparative results are also included in this research for better analysis.https://ieeexplore.ieee.org/document/10189856/BioinformaticsCNNcomputer visiondeep learningimage processingLBP |
| spellingShingle | Lubna Riaz Hafiz Muhammad Qadir Ghulam Ali Mubashir Ali Muhammad Ahsan Raza Anca D. Jurcut Jehad Ali A Comprehensive Joint Learning System to Detect Skin Cancer IEEE Access Bioinformatics CNN computer vision deep learning image processing LBP |
| title | A Comprehensive Joint Learning System to Detect Skin Cancer |
| title_full | A Comprehensive Joint Learning System to Detect Skin Cancer |
| title_fullStr | A Comprehensive Joint Learning System to Detect Skin Cancer |
| title_full_unstemmed | A Comprehensive Joint Learning System to Detect Skin Cancer |
| title_short | A Comprehensive Joint Learning System to Detect Skin Cancer |
| title_sort | comprehensive joint learning system to detect skin cancer |
| topic | Bioinformatics CNN computer vision deep learning image processing LBP |
| url | https://ieeexplore.ieee.org/document/10189856/ |
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