J Med Syst - An improved decision support system for detection of lesions in mammograms using Differential Evolution Optimized Wavelet Neural Network.

Tópicos

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Resumo

In this paper, a computerized scheme for automatic detection of cancerous lesion in mammograms is examined. Breast lesions in mammograms are an area with an abnormality or alteration in the breast tissues. Diagnosis of these lesions at the early stage is a very difficult task as the cancerous lesions are embedded in normal breast tissue structures. This paper proposes a supervised machine learning algorithm - Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of tumor masses in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture features of the abnormal breast tissues and normal breast tissues prior to classification. Then DEOWNN classifier is applied at the end to determine whether the given input data is normal or abnormal. The performance of the computerized decision support system is evaluated using a mini database from Mammographic Image Analysis Society (MIAS). The detection performance is evaluated using Receiver Operating Characteristic (ROC) curves. The result shows that the proposed algorithm has a sensitivity of 96.9% and specificity of 92.9%.

Resumo Limpo

paper computer scheme automat detect cancer lesion mammogram examin breast lesion mammogram area abnorm alter breast tissu diagnosi lesion earli stage difficult task cancer lesion embed normal breast tissu structur paper propos supervis machin learn algorithm differenti evolut optim wavelet neural network deownn detect tumor mass mammogram differenti evolut de popul base optim algorithm base principl natur evolut optim real paramet real valu function util de algorithm paramet wavelet neural network wnn optim increas detect accuraci featur extract methodolog use extract textur featur abnorm breast tissu normal breast tissu prior classif deownn classifi appli end determin whether given input data normal abnorm perform computer decis support system evalu use mini databas mammograph imag analysi societi mia detect perform evalu use receiv oper characterist roc curv result show propos algorithm sensit specif

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