- author = "Jia Meng and Guangxing Wang and Lingxi Zhou and Shenyi Jiang and Shuhao Qian and Lingmei Chen and Chuncheng Wang and Rushan Jiang and Chen Yang and Bo Niu and Yijie Liu and Zhihua Ding and Shuangmu Zhuo and Zhiyi Liu",
- title = "Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning",
- journal = "Journal of Biomedical Optics",
- year = "2023",
- volume = "28",
- number = "4",
- pages = "045001",
- month = "8 " # apr,
- keywords = "genetic algorithms, genetic programming, TPOT, morphological feature, textural feature, machine learning, classification, keloid scar, multiphoton imaging",
-
DOI = "
doi:10.1117/1.JBO.28.4.045001",
- size = "14 pages",
-
abstract = "Significance
Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.
Aim
Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.
Approach
Multiphoton microscopy was utilized to acquire images of collagen and elastin fibres. Morphological features, histogram, and grey-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT).
Results
The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively.
Conclusions
The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.",
