A Clinical Decision Support System to Identify and Stage Breast Cancer Tumor
G. Norhene, D. Alima, S. Dorra, A. Riadh
Annual Research & Review in Biology · pp. 3440–3458 · Published 19 Jun 2014
10.9734/ARRB/2014/9882Abstract
Aims: An improved Clinical Decision Support System is developed to classify the tumor and identify the different stages of the breast cancer. Methodology: In this paper, we analyze and compare the performance of a developed system that takes the breast density information into account. The advantages of consideration of this breast density information will be highlighted. Our proposal is based on multi-resolution Gray Level and Local Difference (GLLD) for feature extraction. Once the descriptors are extracted, Artificial Neural Network (ANN) are used for classifying the detected masses according to their corresponding stages. Results: The accuracy of the proposed system has been verified and found that the Area Under the Curve (AUC) of 99.5% can be achieved for tumor staging when considering the information of beast density and applying the multi-resolution GLLD as texture descriptor. The proposed system may provide valuable information concerning cancer staging.
References (16)
- 1 Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [DOI]
- 2 A comparative study of texture measures with classification based on featured distributions [DOI]
- 3 Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography [DOI]
- 4 Quantitative Classification of Mammographic Densities and Breast Cancer Risk: Results From the Canadian National Breast Screening Study [DOI]
- 5 TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS [DOI]
- 6 A Multiple Scale Model for Tumor Growth [DOI]
- 7 Computerized image analysis: Estimation of breast density on mammograms [DOI]
- 8 Impact of Breast Density on Computer-Aided Detection for Breast Cancer [DOI]
- 9 MammoSys: A content-based image retrieval system using breast density patterns [DOI]
- 10 The Preponderance of Evidence Supports Computer-aided Detection for Screening Mammography [DOI]
- 11 Change of mammographic density predicts the risk of contralateral breast cancer - a case-control study [DOI]
- 12 Automated Assessment of the Composition of Breast Tissue Revealed on Tissue-Thickness-Corrected Mammography [DOI]
- 13 A new approach to the classification of mammographic masses and normal breast tissue [DOI]
- 14 Influence of Using Manual or Automatic Breast Density Information in a Mass Detection CAD System [DOI]
- 15 Impact of mammographic breast density on computer-assisted detection (CAD) in a breast imaging department [DOI]
- 16 FDDSM: Toward a full field digital mammographic database [DOI]
Cited by 0
No indexed citations yet.
Related research
- Neural Network and Regression Based Model for Cows’ Milk Yield Prediction in Different Climatic Gradients — shares topic coverage
- Modeling of Two-Phase Gas Deviation Factor for Gas-Condensate Reservoir using Artificial Neural Network — shares topic coverage
- Comparative Performance of Multiple Linear Regression and Artificial Neural Network Based Models in Estimation of Evaporation — shares topic coverage
- Prediction of Oilfield Scale formation Using Artificial Neural Network (ANN) — shares topic coverage
- A Review of Artificial Neural Networks for Chemical Process Optimization and Compound Property Prediction — shares topic coverage
Article metrics
Real usage data collected on this platform.
61
Page views
0
PDF downloads
15
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.