Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis
Automated skin lesion classification using machine learning techniques is crucial for early and accurate skin cancer detection. This study proposes a hybrid method combining the Hermite, Radial Fourier–Mellin, and Hilbert transform to extract comprehensive features from skin lesion images. By separa...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/5860 |
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| author | Luis Felipe López-Ávila Josué Álvarez-Borrego |
| author_facet | Luis Felipe López-Ávila Josué Álvarez-Borrego |
| author_sort | Luis Felipe López-Ávila |
| collection | DOAJ |
| description | Automated skin lesion classification using machine learning techniques is crucial for early and accurate skin cancer detection. This study proposes a hybrid method combining the Hermite, Radial Fourier–Mellin, and Hilbert transform to extract comprehensive features from skin lesion images. By separating the images into red, green, and blue (RGB) channels and grayscale, unique textural and structural information specific to each channel is analyzed. The Hermite transform captures localized spatial features, while the Radial Fourier–Mellin and Hilbert transforms ensure global invariance to scale, translation, and rotation. Texture information for each channel is also obtained based on the Local Binary Pattern (<i>LBP</i>) technique. The proposed hybrid transform-based feature extraction was applied to multiple lesion classes using the International Skin Imaging Collaboration (ISIC) 2019 dataset, preprocessed with data augmentation. Experimental results demonstrate that the proposed method improves classification accuracy and robustness, highlighting its potential as a non-invasive AI-based tool for dermatological diagnosis. |
| format | Article |
| id | doaj-art-c87fd4c445574b839a4f896b90dd4522 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c87fd4c445574b839a4f896b90dd45222025-08-20T02:32:50ZengMDPI AGApplied Sciences2076-34172025-05-011511586010.3390/app15115860Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale AnalysisLuis Felipe López-Ávila0Josué Álvarez-Borrego1Centro de Investigación Científica y de Educación Superior de Ensenada, B. C. (CICESE), Carretera Ensenada-Tijuana No. 3918, Zona Playitas, Ensenada 22860, BC, MexicoCentro de Investigación Científica y de Educación Superior de Ensenada, B. C. (CICESE), Carretera Ensenada-Tijuana No. 3918, Zona Playitas, Ensenada 22860, BC, MexicoAutomated skin lesion classification using machine learning techniques is crucial for early and accurate skin cancer detection. This study proposes a hybrid method combining the Hermite, Radial Fourier–Mellin, and Hilbert transform to extract comprehensive features from skin lesion images. By separating the images into red, green, and blue (RGB) channels and grayscale, unique textural and structural information specific to each channel is analyzed. The Hermite transform captures localized spatial features, while the Radial Fourier–Mellin and Hilbert transforms ensure global invariance to scale, translation, and rotation. Texture information for each channel is also obtained based on the Local Binary Pattern (<i>LBP</i>) technique. The proposed hybrid transform-based feature extraction was applied to multiple lesion classes using the International Skin Imaging Collaboration (ISIC) 2019 dataset, preprocessed with data augmentation. Experimental results demonstrate that the proposed method improves classification accuracy and robustness, highlighting its potential as a non-invasive AI-based tool for dermatological diagnosis.https://www.mdpi.com/2076-3417/15/11/5860skin lesion classificationHermite transformradial Fourier–Mellin transform |
| spellingShingle | Luis Felipe López-Ávila Josué Álvarez-Borrego Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis Applied Sciences skin lesion classification Hermite transform radial Fourier–Mellin transform |
| title | Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis |
| title_full | Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis |
| title_fullStr | Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis |
| title_full_unstemmed | Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis |
| title_short | Hybrid Transform-Based Feature Extraction for Skin Lesion Classification Using RGB and Grayscale Analysis |
| title_sort | hybrid transform based feature extraction for skin lesion classification using rgb and grayscale analysis |
| topic | skin lesion classification Hermite transform radial Fourier–Mellin transform |
| url | https://www.mdpi.com/2076-3417/15/11/5860 |
| work_keys_str_mv | AT luisfelipelopezavila hybridtransformbasedfeatureextractionforskinlesionclassificationusingrgbandgrayscaleanalysis AT josuealvarezborrego hybridtransformbasedfeatureextractionforskinlesionclassificationusingrgbandgrayscaleanalysis |