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An Automated brain image analysis System for brain Cancer using Shearlets

Computer Systems Science & Engineering 2022年第1期40卷 299-312页

作者： R.Muthaiyan Dr M.Malleswaran Department of Electronics and Communication Engineering University College of Engineering ThirukkuvalaiTamilnadu610204India Department of Electronics and Communication Engineering University College of Engineering KancheepuramKancheepuram631552India

In this paper,an Automated brain image analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is *** classification of MRI images into normal or low grade or high grade plays a vital role for the early *** Non-Subsampled Shearlet Transform(NSST)that captures more visual information than conventional wavelet transforms is employed for feature *** the feature space of NSST is very high,a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies.A combination of features that includes Gray Level Co-occurrence Matrix(GLCM)based features,Histograms of Positive Shearlet Coefficients(HPSC),and Histograms of Negative Shearlet Coefficients(HNSC)are *** combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers;k-Nearest Neighbor(kNN),Naive Bayes(NB)and Support Vector Machine(SVM)*** output of individual trained classifiers for a testing input is hybridized to take a final *** quantitative results of ABIA system on Repository of Molecular brain Neoplasia Data(REMBRANDT)database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

关键词： brain image analysis wavelets Shearlet multi-scale analysis hybrid classification

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Multi Class brain Cancer Prediction System Empowered with BRISK Descriptor

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Intelligent Automation & Soft Computing 2023年第5期36卷 1507-1521页

作者： Madona B.Sahaai G.R.Jothilakshmi E.Praveen V.Hemath Kumar Department of Electronics and Communication Engineering VISTASChennaiTamil NaduIndia

Magnetic Resonance Imaging(MRI)is one of the important resources for identifying abnormalities in the human *** work proposes an effective Multi-Class Classiﬁcation(MCC)system using Binary Robust Invariant Scalable Keypoints(BRISK)as texture descriptors for effective classiﬁ***ﬁrst,the potential Region Of Interests(ROIs)are detected using features from the acceler-ated segment test ***,non-maxima suppression is employed in scale space based on the information in the *** discriminating power of BRISK is examined using three machine learning classiﬁers such as k-Nearest Neighbour(kNN),Support Vector Machine(SVM)and Random Forest(RF).An MCC sys-tem is developed which classiﬁes the MRI images into normal,glioma,meningio-ma and pituitary.A total of 3264 MRI brain images are employed in this study to evaluate the proposed MCC *** show that the average accuracy of the proposed MCC-RF based system is 99.62%with a sensitivity of 99.16%and spe-ciﬁcity of 99.75%.The average accuracy of the MCC-kNN system is 93.65%and 97.59%by the MCC-SVM based system.

关键词： braincancer BRISKdescriptor randomforest multi-classclassiﬁcation brain image analysis

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