Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection
The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle stages. Therefore, an automated model could be developed that assists with early skin cancer detection. It is possible to limit the severity of melanoma...
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MDPI AG
2025-01-01
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| author | Ranpreet Kaur Hamid GholamHosseini Maria Lindén |
| author_facet | Ranpreet Kaur Hamid GholamHosseini Maria Lindén |
| author_sort | Ranpreet Kaur |
| collection | DOAJ |
| description | The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle stages. Therefore, an automated model could be developed that assists with early skin cancer detection. It is possible to limit the severity of melanoma by detecting it early and treating it promptly. This study aims to develop efficient approaches for various phases of melanoma computer-aided diagnosis (CAD), such as preprocessing, segmentation, and classification. The first step of the CAD pipeline includes the proposed hybrid method, which uses morphological operations and context aggregation-based deep neural networks to remove hairlines and improve poor contrast in dermoscopic skin cancer images. An image segmentation network based on deep learning is then used to extract lesion regions for detailed analysis and calculate the optimized classification features. Lastly, a deep neural network is used to distinguish melanoma from benign lesions. The proposed approaches use a benchmark dataset named International Skin Imaging Collaboration (ISIC) 2020. In this work, two forms of evaluations are performed with the classification model. The first experiment involves the incorporation of the results from the preprocessing and segmentation stages into the classification model. The second experiment involves the evaluation of the classifier without employing these stages i.e., using raw images. From the study results, it can be concluded that a classification model using segmented and cleaned images contributes more to achieving an accurate classification rate of 93.40% with a 1.3 s test time on a single image. |
| format | Article |
| id | doaj-art-c71f6e66475f480eba78506811f0b958 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-c71f6e66475f480eba78506811f0b9582025-08-20T02:12:33ZengMDPI AGSensors1424-82202025-01-0125359410.3390/s25030594Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer DetectionRanpreet Kaur0Hamid GholamHosseini1Maria Lindén2Department of Software Engineering & AI, Media Design School, Auckland 1010, New ZealandSchool of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandDivision of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, SwedenThe most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle stages. Therefore, an automated model could be developed that assists with early skin cancer detection. It is possible to limit the severity of melanoma by detecting it early and treating it promptly. This study aims to develop efficient approaches for various phases of melanoma computer-aided diagnosis (CAD), such as preprocessing, segmentation, and classification. The first step of the CAD pipeline includes the proposed hybrid method, which uses morphological operations and context aggregation-based deep neural networks to remove hairlines and improve poor contrast in dermoscopic skin cancer images. An image segmentation network based on deep learning is then used to extract lesion regions for detailed analysis and calculate the optimized classification features. Lastly, a deep neural network is used to distinguish melanoma from benign lesions. The proposed approaches use a benchmark dataset named International Skin Imaging Collaboration (ISIC) 2020. In this work, two forms of evaluations are performed with the classification model. The first experiment involves the incorporation of the results from the preprocessing and segmentation stages into the classification model. The second experiment involves the evaluation of the classifier without employing these stages i.e., using raw images. From the study results, it can be concluded that a classification model using segmented and cleaned images contributes more to achieving an accurate classification rate of 93.40% with a 1.3 s test time on a single image.https://www.mdpi.com/1424-8220/25/3/594skin cancermelanomaclassificationsegmentationdeep learning |
| spellingShingle | Ranpreet Kaur Hamid GholamHosseini Maria Lindén Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection Sensors skin cancer melanoma classification segmentation deep learning |
| title | Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection |
| title_full | Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection |
| title_fullStr | Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection |
| title_full_unstemmed | Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection |
| title_short | Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection |
| title_sort | advanced deep learning models for melanoma diagnosis in computer aided skin cancer detection |
| topic | skin cancer melanoma classification segmentation deep learning |
| url | https://www.mdpi.com/1424-8220/25/3/594 |
| work_keys_str_mv | AT ranpreetkaur advanceddeeplearningmodelsformelanomadiagnosisincomputeraidedskincancerdetection AT hamidgholamhosseini advanceddeeplearningmodelsformelanomadiagnosisincomputeraidedskincancerdetection AT marialinden advanceddeeplearningmodelsformelanomadiagnosisincomputeraidedskincancerdetection |