Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment
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 employi...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-09-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
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| Online Access: | https://doi.org/10.61822/amcs-2024-0041 |
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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 |
| author_sort | Paulauskaite-Taraseviciene Agne |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2447fc47dbb54481a31e620a4cc2f920 |
| institution | OA Journals |
| issn | 2083-8492 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj-art-2447fc47dbb54481a31e620a4cc2f9202025-08-20T01:47:48ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922024-09-0134461762910.61822/amcs-2024-0041Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance AssessmentPaulauskaite-Taraseviciene Agne0Sutiene Kristina1Dimsa Nojus2Valiukeviciene Skaidra3Artificial Intelligence Centre Kaunas University of Technology K. Barsausko g. 59, 51423Kaunas, LithuaniaDepartment of Mathematical Modeling Kaunas University of Technology Studentu g. 50, 51368Kaunas, LithuaniaFaculty of Informatics Kaunas University of Technology Studentu g. 50, 51368Kaunas, Lithuania Department of Skin and Venereal Diseases Lithuanian University of Health Sciences A. Mickeviciaus g. 9, 44307Kaunas, LithuaniaNumerous 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.https://doi.org/10.61822/amcs-2024-0041skin lesionfeature extractiongraph theorymulti-class predictionshap values. |
| spellingShingle | Paulauskaite-Taraseviciene Agne Sutiene Kristina Dimsa Nojus Valiukeviciene Skaidra Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment International Journal of Applied Mathematics and Computer Science skin lesion feature extraction graph theory multi-class prediction shap values. |
| title | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment |
| title_full | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment |
| title_fullStr | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment |
| title_full_unstemmed | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment |
| title_short | Enhancing Multi–Class Prediction of Skin Lesions with Feature Importance Assessment |
| title_sort | 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 |
| work_keys_str_mv | AT paulauskaitetarasevicieneagne enhancingmulticlasspredictionofskinlesionswithfeatureimportanceassessment AT sutienekristina enhancingmulticlasspredictionofskinlesionswithfeatureimportanceassessment AT dimsanojus enhancingmulticlasspredictionofskinlesionswithfeatureimportanceassessment AT valiukevicieneskaidra enhancingmulticlasspredictionofskinlesionswithfeatureimportanceassessment |