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An Efficient Hybrid Classifier for MRI Brain Images Classification Using Machine Learning Based Naive Bayes Algorithm

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Abstract

In recent days, advanced techniques are used to compare the analysis of medical images, identifying, pre-processing and interpreting the images. As a result, visualizing of images have greatly diversified in medical sciences domain. The magnetic resonance imaging (MRI) scanner is commonly used to identify and differentiate between normal and abnormal medical images. But in recent days, analysis of new variant of diseases is very difficult, hence adopting new advanced techniques in data pre-processing and analysis of medical images is very essential. This paper proposes a hybrid naive-Bayes classifier for MRI brain image differentiation process. The image quality of human body parts is enhanced including the brain images to improve classification accuracy rate by efficiently differentiating normal and abnormal images containing the disorders and injuries using a hybrid naive-based classifier in MRI brain images. The image pre-processing, feature extraction and noise reduction is achieved using proposed model. The proposed model is processed in four different steps such as image pre-processing, feature extraction and feature reduction using a naive Bayes classifier. The median filter is effectively utilized by a hybrid algorithm to remove noise such as scalp and skull. The performance analysis has been conducted by collecting a huge number of sample images and efficiently differentiating normal and abnormal images using the proposed algorithm. The comparative analysis has been conducted between proposed algorithm with existing methods like Random subspace with random forest (RS with RF), random subspace with Bayesian Network (RS with BN) and Feed Forward-ANN (FF-ANN). The aim of this work is to improve the classification accuracy with efficient and fast method which identifies the small number set of optimal parameters. The main purpose of the proposed mathematical model is to increase the accuracy rate of normal image classification and abnormal image classification with respect to classification methods like RS with RF, RS with BN and FF-ANN. The proposed hybrid naive Bayes classifier gives a 35–65% splitting ratio for training and splitting ratio. With respect to improvements in normal and abnormal classification of an image, samples are 2%, 3%, and 2.5% using methods (RS with RF), (RS with BN) and feed forward-ANN (FF-ANN), respectively.

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Data availability

Required Datasets for image classification is taken from the Harvard Medical School. Datasets are available in the following link https://spl.harvard.edu/software-and-data-sets.

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  1. GSSS Institute of Engineering & Technology for Women, Mysore, Karnataka, India

    Madhu M. Nayak

  2. Dayananda Sagar Academy of Technology and Management Bengaluru, Bengaluru, Karnataka, India

    Sumithra Devi Kengeri Anjanappa

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This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.

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Nayak, M.M., Kengeri Anjanappa, S.D. An Efficient Hybrid Classifier for MRI Brain Images Classification Using Machine Learning Based Naive Bayes Algorithm. SN COMPUT. SCI. 4, 223 (2023). https://doi.org/10.1007/s42979-022-01614-y

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