Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer(BCa)is a leading cause of death in the female population across the globe.Approximately 2.3 million new BCa cases are recorded globally in females,overtaking lung cancer as the most prevalent form of cancer to be diagnosed.However,the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries.Early diagnosis is the only option to minimize the risks of BCa.Deep 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 images.The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection.This 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)dataset.In preprocessing,median filter and contrast limited adaptive histogram equalization(CLAHE)techniques are utilized for image enhancement.After preprocessing,the segmentation is performed using the region growing(RG)algorithm.The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients(PHOG)and the AlextNet model.Finally,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 evaluated.The 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 has obtained better performances and outperformed the other models.

A hybrid model for energy efficient spectrum sensing in cognitive radio

Purpose-Cognitive radio(CR)plays a very important role in enabling spectral efficiency in wireless communication networks,where the secondary user(SU)allows the licensed primary users(PUs).The purpose of this paper is to develop a prediction model for spectrum sensing in CR.Design/methodology/approach-This paper proposes a hybrid prediction model,called krill-herd whale optimization-based actor critic neural network and hidden Markov model(KHWO-ACNN-HMM).The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs.For better sensing,the eigenvalue based on cooperative sensing used in CR.Finally,a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing.The predicted results of KHWO-ACNN and HMM are combined by a fusion model,for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.Findings-The performance of the prediction model is evaluated based on metrics,such as probability of detection,probability of false alarm,throughput and sensing time.The proposed spectrum sensing method achieves maximum probability of detection of 0.9696,minimum probability of false alarm rate as 0.78,minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.Research implications-The proposed method is useful in various applications,including authentication applications,wireless medical networks and so on.Originality/value-A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models.The proposed hybrid model outperformed the other techniques.