A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.
Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of breast cancer. There are different kinds of methods u...
| Published in: | Journal of Medical Systems Vol. 43; no. 7 |
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| Main Authors: | , |
| Format: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Jul2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137182927&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137182927 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2019 vid: 43 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137182927 137182927 137182927 10.1007/s10916-019-1316-3 137182927 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features. aug: au: Melekoodappattu, Jayesh George Subbian, Perumal Sankar affil: Department of Electronics and Communication Engineering, Vimal Jyothi Engineering College, Kannur, Kerala, India sug: subj: Machine Learning Methods Algorithms Radiographic Image Interpretation, Computer-Assisted Methods Breast Radiography Calcinosis Radiography Mammography Human Digital Imaging Image Processing, Computer Assisted Radiographic Image Enhancement ab: Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of breast cancer. There are different kinds of methods used to detect and recognize micro calcification from mammogram images. This paper presents an ELM (Extreme Learning Machine) algorithm for micro calcification detection in digital mammogram images. The interference of mammographic image is removed at the pre-processing stages. A multi-scale features are extracted by a feature generation model. The performance did not improve by all extracted feature, therefore feature selection is performed by nature-inspired optimization algorithm. At last, the hybridized ELM classifier taken the selected optimal features to classify malignant from benign micro calcifications. The proposed work is compared with various classifiers and it shown better performance in training time, sensitivity, specificity and accuracy. The existing approaches considered here are SVM (Support Vector Machine) and NB (Naïve Bayes classifier). The proposed detection system provides 99.04% accuracy which is the better performance than the existing approaches. The optimal selection of feature vectors and the efficient classifier improves the performance of proposed system. Results illustrate the classification performance is better when compared with several other classification approaches. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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