Automatic detection method of bladder tumor cells based on color and shape features

Autor: Zitong Zhao, Yanbo Wang, Jiaqi Chen, Mingjia Wang, Shulong Feng, Jin Yang, Nan Song, Jinyu Wang, Ci Sun
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Innovative Optical Health Sciences, Vol 17, Iss 06 (2024)
Druh dokumentu: article
ISSN: 17935458
1793-7205
1793-5458
DOI: 10.1142/S1793545824500056
Popis: Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system, and its incidence rate ranks ninth in the world. In recent years, the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer. In this study, based on microscopic hyperspectral data, an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed. Support vector machine (SVM) is used to build classification models and compare the classification performance of spectral feature, spectral and shape fusion feature, and the fusion feature proposed in this paper on the same classifier. The results show that the sensitivity, specificity, and accuracy of our classification algorithm based on shape and color fusion features are 0.952, 0.897, and 0.920, respectively, which are better than the classification algorithm only using spectral features. Therefore, this study can effectively extract the cell features of bladder urothelial carcinoma smear, thus achieving automatic, real-time, and noninvasive detection of bladder tumor cells, and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer, and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje