J Biomed Inform - Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach.

Tópicos

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Resumo

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%.

Resumo Limpo

breast cancer second lead caus cancer death women accur earli detect can effect reduc mortal rate caus breast cancer mass microcalcif cluster import earli sign breast cancer howev often difficult distinguish abnorm normal breast tissu subtl appear ambigu margin comput aid diagnosi cad help radiologist detect abnorm effici way paper investig new classif approach detect breast abnorm digit mammogram use particl swarm optim wavelet neural network psownn propos abnorm detect algorithm base extract law textur energi measur mammogram classifi suspici region appli pattern classifi method appli real clinic databas mammogram collect mammogram screen center detect perform cad system analyz use receiv oper characterist roc curv curv indic tradeoff sensit specif avail diagnost system thus describ inher discrimin capac propos system result show area roc curv propos algorithm sensit specif

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