Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach

• Published in: Journal of biomedical informatics Vol. 49; pp. 45 - 52
• Main Authors: Dheeba, J., Albert Singh, N., Tamil Selvi, S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2014
• Subjects: Abnormalities; Algorithms; Breast; Breast cancer; Breast Neoplasms - diagnostic imaging; Cancer; Classification; Computer aided design; Computer aided diagnosis; Diagnosis, Computer-Assisted; Female; Humans; Laws; Mammograms; Mammography; Neural networks; Neural Networks (Computer); Particle swarm optimization; Receiver operating characteristic; Texture; Wavelet Analysis; Breast cancer; Computer aided diagnosis; Receiver operating characteristic; Laws; Particle swarm optimization; Mammograms
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2014.01.010

PSOWNN – Particle Swarm Optimized Wavelet Neural Network. DB – Database. [Display omitted] •We propose a CAD system for detecting breast cancer in mammograms.•Swarm intelligence optimized wavelet neural network detects the cancers.•We focus on optimized wavelet neural network to enhance the detection accuracy.•Experiments are carried out on real clinical database collected from screening centers.•Our method yielded better performance than other existing approaches. Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.