Computer aided detection and diagnosis for breast Cancer images / Aminah Abdul Malek ... [et al.]

Abdul Malek, Aminah and Mohamed, Norlyda and Mohd Zaki, Noor Hidayah and Mohd Amin, Farah Azaliney and Udin, Md Nizam and Shahril, Rahmah and Selamat, Mat Salim (2020) Computer aided detection and diagnosis for breast Cancer images / Aminah Abdul Malek ... [et al.]. In: UNSPECIFIED.


Breast cancer detection is critically depending on early and accurate diagnosis. Machine learning technique can enhance the level of detection and classification of breast cancer images. Normally, radiologist will look at the potential abnormalities in mammogram and ultrasound images. However, the images are low in contrast and the features indicative of abnormalities are very subtle. Hence, it gives difficulties for radiologist to interpret those images. Therefore, in order to assist radiologist, a Computer Aided Detection and Diagnosis (CADx) is developed. This platform used Seed Based Region Growing (SBRG) as a segmentation technique for extracting a region of the images. For further analysis of the mammogram images, the classification platform was also developed using Enhanced Support Vector Machine (ESVM) that combines Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) methods. The outcomes of this project can help the radiologists by marking the exact location of abnormalities and it is able to differentiate between benign or malignant tumor.


Item Type: Conference or Workshop Item (Paper)
Email / ID Num.
Abdul Malek, Aminah
Mohamed, Norlyda
Mohd Zaki, Noor Hidayah
Mohd Amin, Farah Azaliney
Udin, Md Nizam
Shahril, Rahmah
Selamat, Mat Salim
Subjects: R Medicine > R Medicine (General) > Computer applications to medicine. Medical informatics
R Medicine > RC Internal Medicine > Cancer
R Medicine > RC Internal Medicine > Examination. Diagnosis. Including radiography
Divisions: Universiti Teknologi MARA, Perak
Journal or Publication Title: The 9th International Innovation, Invention and Design Competition 2020
Page Range: pp. 367-369
Keywords: segmentation; classification; seed based region growing; support vector machine
Date: 2020
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