DETECTION OF MICROCALCIFICATION IN MAMMOGRAMS USING AI & ML TECHNIQUES

DETECTION OF MICROCALCIFICATION IN MAMMOGRAMS USING AI & ML TECHNIQUES

Subash Chandra Bose Jaganathan

Autore: Subash Chandra Bose Jaganathan
Formato: Copertina flessibile
Pagine: 168
Data Pubblicazione: 2021-03-31
Edizione: 1
Lingua: English

Descrizione:
One of the new modern and most efficient methods for breast cancer early detection is mammography. A new method for the detection and classification of microcalcifications is presented. It can be done in four stages: first, preprocessing stage deals with noise removal, and normalized the image. The second stage, Fuzzy cMeans clustering (FCM) is used for segmentation and pectoral muscle extraction using area calculation and finally microcalcifications detection. The third stage consists of twodimensional discrete wavelet transforms are extracted from the detection of microcalcifications. And then, nine statistical features are calculated from the LL band of the wavelet transform. Finally, the extracted features are fed as input to the Artificial Neural Network and are classified into normal or abnormal (benign or malignant) images. The given classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. The results are analyzed using MATLAB.