Preprint Technical Note Version 1 This version is not peer-reviewed

Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features

Version 1 : Received: 21 June 2018 / Approved: 21 June 2018 / Online: 21 June 2018 (15:46:03 CEST)

How to cite: Singh, M.; Singh, D.; Sharma, V. Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features. Preprints 2018, 2018060343 (doi: 10.20944/preprints201806.0343.v1). Singh, M.; Singh, D.; Sharma, V. Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features. Preprints 2018, 2018060343 (doi: 10.20944/preprints201806.0343.v1).

Abstract

Breast cancer is the second cause of fatality among all cancers for women. Automatic classification of breast cancer lesions in mammograms is a challenging task due to the irregularity and complexity of the location, size, shape, and texture of these lesions. The intensity dissimilarity has been found between breast cancer tissues and normal tissues, when a multi-spectral anatomical mammographic screening scans have been done. In this work, two approaches have been evaluated to classify the breast tumor lesions. The first one is through Gabor wavelet features and the second one is Statistical features. Subsequently, support vector machine, Multilayer Perceptron and KNN classifiers have been used with computer based method for breast tumor classification.

Subject Areas

Lesion classification; statistical features; mammograms

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