Ngoc-Bao-Tran Nguyen , Quoc-Hoang-Quyen Vo , Thanh-Hai Le , Ngoc-Trinh Huynh , Quoc-Hung Phan , Thi-Thu-Hien Pham
{"title":"穆勒矩阵成像偏振测量法与人工智能相结合,对小鼠皮肤癌组织进行体外和体内分类","authors":"Ngoc-Bao-Tran Nguyen , Quoc-Hoang-Quyen Vo , Thanh-Hai Le , Ngoc-Trinh Huynh , Quoc-Hung Phan , Thi-Thu-Hien Pham","doi":"10.1016/j.ijleo.2024.171932","DOIUrl":null,"url":null,"abstract":"<div><p>Mueller matrix imaging polarimetry is a fast and non-invasive technique for discriminating between different types of biological samples based on the characteristics of polarized light interacting with them. Combining Mueller matrix imaging polarimetry with artificial intelligence provides further advantages in detecting different kinds of medical conditions in an automated manner. Accordingly, the present study proposes a method based on Mueller matrix polarimetry and machine learning algorithms for discriminating between (1) four different types of mice skin tissues (normal, acanthosis, papilloma, and squamous cell carcinoma); (2) two types of mice skin tissues which show histological similarities to human equivalents (normal and squamous cell carcinoma); and (3) 7,12-dimethylbenz[<em>a</em>]anthracene/estrogen-induced mice skin tissues. Five machine learning classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting, and Gaussian Naïve Bayes, are considered in the first and second applications, while three models (Support Vector Machine, K-Nearest Neighbor, Gradient Boosting) are considered in the third application. For each application, the features which dominate the machine learning prediction performance are determined through multivariate correlation matrix analysis, kernel density estimation, and Analysis of Variance tests. The experimental results show that the Random Forest model achieves the highest classification accuracy (93.55 %) for the first application, while the Support Vector Machine model yields the highest accuracy for both the second and third applications (97.66 % and 100 %, respectively). Overall, the proposed framework consisting of Mueller matrix imaging polarimetry and machine learning provides a strong foundation for the on-going development of screening and diagnosis methods for human skin cancer.</p></div>","PeriodicalId":19513,"journal":{"name":"Optik","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combination of Muller matrix imaging polarimetry and artificial intelligence for classification of mice skin cancer tissue in-vitro and in-vivo\",\"authors\":\"Ngoc-Bao-Tran Nguyen , Quoc-Hoang-Quyen Vo , Thanh-Hai Le , Ngoc-Trinh Huynh , Quoc-Hung Phan , Thi-Thu-Hien Pham\",\"doi\":\"10.1016/j.ijleo.2024.171932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Mueller matrix imaging polarimetry is a fast and non-invasive technique for discriminating between different types of biological samples based on the characteristics of polarized light interacting with them. Combining Mueller matrix imaging polarimetry with artificial intelligence provides further advantages in detecting different kinds of medical conditions in an automated manner. Accordingly, the present study proposes a method based on Mueller matrix polarimetry and machine learning algorithms for discriminating between (1) four different types of mice skin tissues (normal, acanthosis, papilloma, and squamous cell carcinoma); (2) two types of mice skin tissues which show histological similarities to human equivalents (normal and squamous cell carcinoma); and (3) 7,12-dimethylbenz[<em>a</em>]anthracene/estrogen-induced mice skin tissues. Five machine learning classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting, and Gaussian Naïve Bayes, are considered in the first and second applications, while three models (Support Vector Machine, K-Nearest Neighbor, Gradient Boosting) are considered in the third application. For each application, the features which dominate the machine learning prediction performance are determined through multivariate correlation matrix analysis, kernel density estimation, and Analysis of Variance tests. The experimental results show that the Random Forest model achieves the highest classification accuracy (93.55 %) for the first application, while the Support Vector Machine model yields the highest accuracy for both the second and third applications (97.66 % and 100 %, respectively). Overall, the proposed framework consisting of Mueller matrix imaging polarimetry and machine learning provides a strong foundation for the on-going development of screening and diagnosis methods for human skin cancer.</p></div>\",\"PeriodicalId\":19513,\"journal\":{\"name\":\"Optik\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optik\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030402624003310\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optik","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030402624003310","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Combination of Muller matrix imaging polarimetry and artificial intelligence for classification of mice skin cancer tissue in-vitro and in-vivo
Mueller matrix imaging polarimetry is a fast and non-invasive technique for discriminating between different types of biological samples based on the characteristics of polarized light interacting with them. Combining Mueller matrix imaging polarimetry with artificial intelligence provides further advantages in detecting different kinds of medical conditions in an automated manner. Accordingly, the present study proposes a method based on Mueller matrix polarimetry and machine learning algorithms for discriminating between (1) four different types of mice skin tissues (normal, acanthosis, papilloma, and squamous cell carcinoma); (2) two types of mice skin tissues which show histological similarities to human equivalents (normal and squamous cell carcinoma); and (3) 7,12-dimethylbenz[a]anthracene/estrogen-induced mice skin tissues. Five machine learning classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting, and Gaussian Naïve Bayes, are considered in the first and second applications, while three models (Support Vector Machine, K-Nearest Neighbor, Gradient Boosting) are considered in the third application. For each application, the features which dominate the machine learning prediction performance are determined through multivariate correlation matrix analysis, kernel density estimation, and Analysis of Variance tests. The experimental results show that the Random Forest model achieves the highest classification accuracy (93.55 %) for the first application, while the Support Vector Machine model yields the highest accuracy for both the second and third applications (97.66 % and 100 %, respectively). Overall, the proposed framework consisting of Mueller matrix imaging polarimetry and machine learning provides a strong foundation for the on-going development of screening and diagnosis methods for human skin cancer.
期刊介绍:
Optik publishes articles on all subjects related to light and electron optics and offers a survey on the state of research and technical development within the following fields:
Optics:
-Optics design, geometrical and beam optics, wave optics-
Optical and micro-optical components, diffractive optics, devices and systems-
Photoelectric and optoelectronic devices-
Optical properties of materials, nonlinear optics, wave propagation and transmission in homogeneous and inhomogeneous materials-
Information optics, image formation and processing, holographic techniques, microscopes and spectrometer techniques, and image analysis-
Optical testing and measuring techniques-
Optical communication and computing-
Physiological optics-
As well as other related topics.