Mammogram images classification based on fuzzy soft set

• 第一著者: Anwar Lashari, Saima
• フォーマット: 学位論文
• 言語: English; English; English
• 出版事項: 2016
• 主題: QA Mathematics; QA150-272.5 Algebra
• オンライン･アクセス: http://eprints.uthm.edu.my/10041/2/24p%20SAIMA%20ANWAR%20LASHARI.pdf; http://eprints.uthm.edu.my/10041/1/SAIMA%20ANWAR%20LASHARI%20COPYRIGHT%20DECLARATION.pdf; http://eprints.uthm.edu.my/10041/3/SAIMA%20ANWAR%20LASHARI%20WATERMARK.pdf; http://eprints.uthm.edu.my/10041/

Early detection of the breast cancer can decrease mortality rates. Screening mammography is considered the most reliable method in early detection of breast cancer. Due to the high volume of mammograms to be read by a physician, the accuracy rate tends to decrease. Thus, automatic digital mammograms reading becomes highly enviable, it is premised that the computer aided diagnosis systems are required to assist physicians/radiologists to achieve high efficiency and effectiveness. Meanwhile, recent advances in the field of image processing have revealed that level of noise highly affect the mammogram images quality and classification performance of the classifiers. Therefore, this study investigates the functionality of wavelet de-noising filters for improving images quality. The dataset taken from Mammographic Image Analysis Society (MIAS). The best PSNR and MSE values 46.36423dB (hard thresholding) and I .827967 achieved with Daub3 filter. Whilst, several medical imaging modalities and applications based on data mining techniques have been proposed and developed. However, fuzzy soft set theory has been merely experimented for medical images even though the choice of convenient parameterization makes fuzzy soft set practicable for decision making applications. Therefore, the viability of fuzzy soft set for classification of mammograms images has been scrutinized. Experimental results show better classification performance in the presence/absence of de-noise filter in mammogram images where the highest classification rate occurs with Daub3 (Level l) with accuracy 75.64% (hard threshold), precision 46.11 %, recall 84.67%, F-Macro 75.64%, F-Micro 60% and performance of FussCyier without de-noise filter classification accuracy 66.49%, precision 80.83%, recall 50% and F-Micro 68.18%. Thus, the results show that proposed approach FussCyier gives high level of accuracy and reduce the complexity of the classification phase, thus provides an alternative technique to categorize mammogram images