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Classification of Multi-view Digital Mammogram Images Using smo-wknn

Computer Systems Science & Engineering 2023年第8期46卷 1741-1758页

作者： P.Malathi G.Charlyn Pushpa Latha Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Breast cancer(BCa)is a leading cause of death in the female population across the *** 2.3 million new BCa cases are recorded globally in females,overtaking lung cancer as the most prevalent form of cancer to be ***,the mortality rates for cervical and BCa are significantly higher in developing nations than in developed *** diagnosis is the only option to minimize the risks of *** learning(DL)-based models have performed well in image processing in recent years,particularly convolutional neural network(CNN).Hence,this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram *** main objective of this research is to develop an accurate and efficient early diagnosis model for BCa *** proposed model is a multi-view-based computer-aided diagnosis(CAD)model,which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique(MLO)and cranio-caudal(CC).The digital mammogram images are collected from the digital database for screening mammography(DDSM)*** preprocessing,median filter and contrast limited adaptive histogram equalization(CLAHE)techniques are utilized for image *** preprocessing,the segmentation is performed using the region growing(RG)*** feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients(PHOG)and the AlextNet ***,the classification is performed using the weighted k-nearest neighbor(wknn)optimized with sequential minimal optimization(smo).The classified images are evaluated based on accuracy,recall,precision,specificity,f1-score,and mathews correlation coefficient(MCC).Additionally,the false positive and error rates are *** proposed model obtained 98.57%accuracy,98.61%recall,99.25%specificity,98.63%precision,97.93%f1-score,96.26%MCC,0.0143 error rate,and 0.0075 false positive rate(FPR).Compared to the existing models,the research model ha

关键词： Breast cancer DDSM CLAHE median filter region growing PHOG AlexNet smo-wknn

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