Open AccessAutomated Classification of Breast Cancer Lesions for Digitised Mammograms via Computer-Aided Diagnosis System
Author(s) -
Saifullah Harith Suradi,
Kamarul Amin Abdullah,
Nor Ashidi Mat Isa
Publication year - 2021
Publication title -
journal of applied science and process engineering
Language(s) - English
Resource type - Journals
ISSN - 2289-7771
DOI - 10.33736/jaspe.3517.2021
Subject(s) - breast cancer , thresholding , cad , computer aided diagnosis , artificial intelligence , mammography , segmentation , block (permutation group theory) , medicine , histogram equalization , computer science , computer vision , image processing , pattern recognition (psychology) , adaptive histogram equalization , histogram , cancer , mathematics , image (mathematics) , geometry , engineering drawing , engineering
Women with breast cancer have a high risk of death. Digitised mammograms can be used to detect the early stage of breast cancer. However, digitised mammograms suffer low contrast appearances that may lead to misdiagnosis. This paper proposes a Computer-Aided Diagnosis (CAD) system of automated classification of breast cancer lesions using a modified image processing technique of Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) incorporated with Multilevel Otsu Thresholding on digitised mammograms. Four main blocks were used in this CAD system, namely; (i) Pre-processing and Enhancement block; (ii) Segmentation block; (iii) Region of Interests (ROIs) Extraction block; and (iv) Classification block. The CAD system was tested on 30 digitised mammograms retrieved from the Mini-Mammographic Image Analysis Society (MIAS) database with various degrees of severity and background tissues. The proposed CAD system showed a high accuracy of 96.67% for the detection of breast cancer lesions.