LBP–Bilateral Based Feature Fusion for Breast Cancer Diagnosis

Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast cancer are needed.The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy.Machine learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many patients.There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising results.The proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia.This paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a classifier.The NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to SVM.Adaboost has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.

Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

Breast cancer(BC)is the most common cause of women’s deaths worldwide.The mammography technique is the most important modality for the detection of BC.To detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a baseline.The correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for specialists.In this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia.The collection of all categories of BI-RADs is one of the major contributions of this paper.Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM.The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results.This ensemble model saves the values to integrate them with SVM.The proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,respectively.The proposed model achieved better performance compared to previously available methods.