Classification of non-tumorous skin pigmentation disorders using voting based probabilistic linear discriminant analysis

• Main Authors: Liang, Yunfeng, Sun, Lei, Ser, Wee, Lin, Feng, Thng, Steven Tien Guan, Chen, Qiping, Lin, Zhiping
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2020
• 主題: Engineering::Computer science and engineering; Engineering::Electrical and electronic engineering; V-PLDA; Non-tumorous
• 在線閱讀: https://hdl.handle.net/10356/136676

Non-tumorous skin pigmentation disorders can have a huge negative emotional impact on patients. The correct diagnosis of these disorders is essential for proper treatments to be instituted. In this paper, we present a computerized method for classifying five non-tumorous skin pigmentation disorders (i.e., freckles, lentigines, Hori's nevus, melasma and nevus of Ota) based on probabilistic linear discriminant analysis (PLDA). To address the large within-class variance problem with pigmentation images, a voting based PLDA (V-PLDA) approach is proposed. The proposed V-PLDA method is tested on a dataset that contains 150 real-world images taken from patients. It is shown that the proposed V-PLDA method obtains significantly higher classification accuracy (4% or more with p< 0.001 in the analysis of variance (ANOVA) test) than the original PLDA method, as well as several state-of-the-art image classification methods. To the authors' best knowledge, this is the first study that focuses on the non-tumorous skin pigmentation image classification problem. Therefore, this paper could provide a benchmark for subsequent research on this topic. Additionally, the proposed V-PLDA method demonstrates promising performance in clinical applications related to skin pigmentation disorders.