Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment
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2024
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| author | Paulauskaite-Taraseviciene, Agne Sutiene, Kristina Dimsa, Nojus Valiukeviciene, Skaidra |
| author_facet | Paulauskaite-Taraseviciene, Agne Sutiene, Kristina Dimsa, Nojus Valiukeviciene, Skaidra |
| contents | <p>Numerous image processing techniques have been developed for the identification of various types of skin lesions. In real-world scenarios, the specific lesion type is often unknown in advance, leading to a multi-class prediction challenge. The available evidence underscores the importance of employing a comprehensive array of diverse features and subsequently identifying the most important ones as a crucial step in visual diagnostics. For this purpose, we addressed both binary and five-class classification tasks using a small dataset, with skin lesions prevalent in Lithuania. The model was trained using a rich set of 662 features, encompassing both conventional image features and graph-based ones, which were obtained from the superpixel graph generated using Delaunay triangulation. We explored the influence of feature importance determined by SHAP values, resulting in a weighted F1-score of 92.48% for the two-class classification and 71.21% for the five-class prediction.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_61822_amcs-2024-0041 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment Paulauskaite-Taraseviciene, Agne Sutiene, Kristina Dimsa, Nojus Valiukeviciene, Skaidra skin lesion feature extraction Graph theory multi-class prediction SHAP values <p>Numerous image processing techniques have been developed for the identification of various types of skin lesions. In real-world scenarios, the specific lesion type is often unknown in advance, leading to a multi-class prediction challenge. The available evidence underscores the importance of employing a comprehensive array of diverse features and subsequently identifying the most important ones as a crucial step in visual diagnostics. For this purpose, we addressed both binary and five-class classification tasks using a small dataset, with skin lesions prevalent in Lithuania. The model was trained using a rich set of 662 features, encompassing both conventional image features and graph-based ones, which were obtained from the superpixel graph generated using Delaunay triangulation. We explored the influence of feature importance determined by SHAP values, resulting in a weighted F1-score of 92.48% for the two-class classification and 71.21% for the five-class prediction.</p> |
| title | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment |
| topic | skin lesion feature extraction Graph theory multi-class prediction SHAP values |
| url | https://doi.org/10.61822/amcs-2024-0041 |