A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

Autor: Esma Uzunhisarcikli, Volkan Göreke
Přispěvatelé: [Uzunhisarcikli, Esma] Erciyes Univ, Kayseri Vocat Coll, Kayseri, Turkey -- [Goreke, Volkan] Cumhuriyet Univ, Kangal Vocat Coll, Sivas, Turkey
Rok vydání: 2018
Předmět:
Zdroj: Sādhanā. 43
ISSN: 0973-7677
0256-2499
DOI: 10.1007/s12046-018-0915-x
Popis: WOS: 000438769300003
Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a two-layer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.
Erciyes University Scientific Research Projects [6629]
This study is supported by Erciyes University Scientific Research Projects unit with the ID of 6629. The authors are gratefull to Dr T S A Geertsma for supplying the ultrasound image database and Dr Bilge Oztoprak (Cumhuriyet University, Faculty of Medicine, Radiology Department) for commenting on ultrasound images medically.
Databáze: OpenAIRE
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