Automated Classification of Breast Cancer Lesions for Digitised Mammograms via Computer-Aided Diagnosis System

Suradi, Saifullah Harith; Abdullah, Kamarul Amin; Isa, Nor Ashidi Mat

doi:10.33736/jaspe.3517.2021

Cited by 1 publication

(1 citation statement)

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“…Finally, CVM classi er technique provides the highest accuracy rate, which was 95.89%, using MIAS dataset. S. Suradi et al, in 2021, developed a CAD system for the breast cancer diagnosis [21]. Otsu thresholding technique was applied to segment the ROI, and then features such as area, circularity, and solidity were extracted from the ROI.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification System for Early Breast Cancer Diagnosis using Machine Learning

Aziz,

Abdulla

2024

Preprint

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Morbidity and even mortality are common outcomes of breast cancer, which is one of the most common diseases affecting women worldwide. Breast cancer is challenging, expensive, and takes a long time to diagnose manually by radiologist. Since it gets beyond all of the drawbacks of manual diagnosis, an automatic/computer-based diagnosis of breast cancer might be thought of as an alternative to manual diagnosis. Utilizing image processing techniques, computer-based diagnostic systems process breast images from mammograms. This study aims to suggest a computer-based diagnostic system for breast cancer by using machine learning to classify the input mammography image into three classes: normal, benign, and malignant. The suggested system comprises of a certain of steps. The input image is initially pre-processed to remove labels and enhance image quality using median filter and adaptive histogram equalization. The next step entails applying the threshold segmentation technique to segment the cancer cells in order to isolate the region of interest (ROI). The Gray Level Run Length Matrix (GLRLM) feature extraction technique is then implemented to extract texture features from the segmented ROI. Consequently, on the basis of the extracted features, Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) classifier techniques are employed to classify the segmented region as normal, benign, and malignant. The performance of the proposed system was examined via extensive experiments conducted on the well-known Mammographic Image Analysis Society (MIAS) dataset of mammography images. The experimental findings reveal that the proposed system outperforms existing systems, which attained a 98.6% accuracy rate.

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Section: Literature Reviewmentioning

confidence: 99%