A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features
| Autor: | Perumal Sankar Subbian, Jayesh George Melekoodappattu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Medicine (miscellaneous) Health Informatics CAD Feature selection Machine Learning Naive Bayes classifier Health Information Management medicine Mammography Humans Diagnosis Computer-Assisted Extreme learning machine medicine.diagnostic_test business.industry Calcinosis Pattern recognition Support vector machine Radiographic Image Enhancement ComputingMethodologies_PATTERNRECOGNITION Female Artificial intelligence business Classifier (UML) Algorithms Information Systems |
| Zdroj: | Journal of medical systems. 43(7) |
| ISSN: | 1573-689X |
| Popis: | 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 (Naive 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink