Creator:

Paulauskaite-Taraseviciene, Agne ; Sutiene, Kristina ; Dimsa, Nojus ; Valiukeviciene, Skaidra

Contributor:

Woźniak, Marcin - ed. ; Kumar, Yogesh - ed. ; Ijaz, Muhammad Fazal - ed.

Title:

Enhancing multi-class prediction of skin lesions with feature importance assessment

Subtitle:

.

Group publication title:

AMCS, volume 34 (2024)

Subject and Keywords:

skin lesion ; feature extraction ; graph theory ; multi-class prediction ; SHAP values

Abstract:

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2024

Resource Type:

artykuł

DOI:

10.61822/amcs-2024-0041

Pages:

617-629

Source:

AMCS, volume 34, number 4 (2024) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego