An Approach for PCA and GLCM Based MRI Image Classification

Shirke, Sheetal S.; Kendule, Jyoti A.; Vyawhare, Samata G.

doi:10.1007/978-3-319-53556-2_26

Cited by 3 publications

(2 citation statements)

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“…The purpose of the feature selection is to improve prediction performance, provide faster and better prediction, and provide a better understanding of the information production pathway [Guyon and Elisseeff (2003)]. Shirke et al [Shirke, Kendule and Vyawhare (2016)] have designed and evaluated in new approach to overcome the disadvantages of previous methods that can diagnose and categorize MRI images of normal and abnormal images. The proposed method divides MRI images into natural bands, benign tumors, and malignant tumors using a probabilistic neural network with radial base function.…”

Section: Image Classificationmentioning

confidence: 99%

“…One of the most important contributions for doctors and patients in this area is the use of computer systems to categorize these images. This can help you better diagnose your doctor and speed it up [Shirke, Kendule and Vyawhare (2016); Shenbagarajan, Ramalingam, Balasubramanian et al (2016)]. One of the issues that concerns MRI images is the discussion of the correct diagnosis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

Nejad¹,

Ahmadabadi²

2019

Computer Modeling in Engineering &Amp; Sciences

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The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this paper, an effective method is proposed for categorizing MRI images, which emphasizes the classification stage. In this method, images have been firstly collected and tagged, and then the discrete wavelet transform method has been implemented to extract the relevant properties. All the ready features in a matrix will be subsequently held, and PCA method has been applied to reduce the features dimension. Furthermore, a new model using support vector machine classifier with radial basis function kernel i.e. SVM+RBF has been performed. The SVM Algorithm must bimanually initialized, while, these values have been automatically entered into the SVM classifier by Salp Swarm Algorithm (SSA): Due to high performance of SSA in fast and accurate solution of nonlinear problem as compared to other optimization algorithms, it has been applied to optimally solve the designed problem. Finally, after applying the optimal parameters and SVM classification training, the test data has been utilized and evaluated. The results have transparently suggested the effectiveness of the proposed method in the Accuracy criteria with 0.9833, the Sensitivity with 1, Specificity with 0.9818 and Error with 0.0167 in best iteration as compared to the conventional SVM method.

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Section: Image Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%