Curvelet Transform-Local Binary Pattern Feature Extraction Technique for Mass Detection and Classification in Digital Mammogram

Tosin, Adeyemo; Morufat, Adepoju; Omotayo, O.; Bolanle, Wahab Wajeed; Olusayo, Omidiora Elijah; Olatunde, Olabiyisi Stephen

doi:10.9734/cjast/2018/42579

Cited by 5 publications

(1 citation statement)

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“…In the work of Tosin et al, (2018), a curvelet Transform (CT)-Local Binary Pattern (LBP) feature extraction technique was proposed to detect and classify mass in digital mammogram. The MIAS dataset was used for the research and an accuracy of 94.17% was achieved.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Model for Breast Cancer Detection and Classification from Mammograms

Adekiigbe¹,

Adepoju²,

Oladele³

2022

ijird

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Breast cancer is a type of disease which is common to women. It is an abnormal behaviour which hinders the functionality of the normal breast cells which leads to forming a tumor in the form of lump. Early detection of breast cancers tends to reduce death risk and provides a chance of a suitable and better treatment. A two-stage classification model was proposed to detect and classify breast cancer using two publicly available datasets namely MIAS and the DDSM dataset. The first classification stage uses Convolution Neural Network (CNN) to classify the mammogram image as either normal or abnormal while K-Nearest Neighbour (KNN) was used for the second classification stage to classify the abnormal image into benign or malignant.The features used for the second stage classification were extracted using Gray Level Co-occurrence Matrix (GLCM), which was later used for classification. The performance was evaluated based on accuracy, sensitivity, specificity, precision, False Positive Rate (FPR), F1 score and Matthews Correlation Coefficient (MCC). The results show that CNN obtained the highest accuracy, sensitivity, specificity,

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Section: Related Workmentioning

confidence: 99%