ArticleJournal of medical systems2016
An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 51 citations in OpenAlex.
- A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis.Open life sciences · 2022Review
- Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.Journal of grid computing · 2022Article
- Computational Methods for Structure-Based Drug Design Through System Biology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms.Frontiers in oncology · 2021Article
- 3D Deep Learning on Medical Images: A Review.Sensors (Basel, Switzerland) · 2020Review
- Spatially localized sparse representations for breast lesion characterization.Computers in biology and medicine · 2020Article
- Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.Radiology · 2019Review
- Levels Propagation Approach to Image Segmentation: Application to Breast MR Images.Journal of digital imaging · 2019Article
- Radiological images and machine learning: Trends, perspectives, and prospects.Computers in biology and medicine · 2019Review
- Microcalcification Segmentation from Mammograms: A Morphological Approach.Journal of digital imaging · 2017Article
- HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images.Journal of medical systems · 2016Article
- A Computer-Aided Detection System for Digital Chest Radiographs.Journal of healthcare engineering · 2016Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, a novel framework of computer-aided diagnosis (CAD) system has been presented for the classification of benign/malignant breast tissues. The properties of the generalized pseudo-Zernike moments (GPZM) and pseudo-Zernike moments (PZM) are utilized as suitable texture descriptors of the suspicious region in the mammogram. An improved classifier- adaptive differential evolution wavelet neural network (Ada-DEWNN) is proposed to improve the classification accuracy of the CAD system. The efficiency of the proposed system is tested on mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on mammograms from the Digital Database for Screening Mammography (DDSM) database using 10-fold cross validation. The proposed method on MIAS-database attains a fair accuracy of 0.8938 and AUC of 0.935 (95 % CI = 0.8213-0.9831). The proposed method is also tested for in-plane rotation and found to be highly rotation invariant. In addition, the proposed classifier is tested and compared with some well-known existing methods using receiver operating characteristic (ROC) analysis using DDSM- database. It is concluded the proposed classifier has better area under the curve (AUC) (0.9289) and highly précised with 95 % CI, 0.8216 to 0.9834 and 0.0384 standard error.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.