Classification of Different Medical Images Using Neural Network Approach

Ujwala Ghodeswar; Anchal Borkar; Ashutosh Bagde

doi:10.17485/IJST/v15i46.1160

Article

Classification of Different Medical Images Using Neural Network Approach

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i46.1160

Year: 2022, Volume: 15, Issue: 46, Pages: 2555-2561

Original Article

Classification of Different Medical Images Using Neural Network Approach

Ujwala Ghodeswar¹, Anchal Borkar¹, Ashutosh Bagde^2*

¹Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
²Datta Meghe Institute of Medical Sciences, Sawangi, Wardha, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:12 August 2022, Accepted Date:21 September 2022, Published Date:14 December 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This work aims to design model for classification and selection of medical images by using the Convolution Neural Network technique with higher accuracy. Methods: Classification of the digital images into relevant categories like X-ray, CT, MRI is implemented using convolution neural network. At the initial stage total 7560 different medical images are given as input. These images are applied to the classifier. These images are passed through different levels of CNN. Findings: This method identifies medical images and separates into different categories i.e., X-ray, CT, MRI using convolution neural network. Accuracy calculated using this method is 99.01%. This method gives better results as compared to other machine learning methods i.e. Support vector machines. Program is written in python language using Jupiter Notebook. Novelty: Total 7560 images of different category are given as input. Convolutional Neural network approach gives good accuracy of 99.01% as compared to other machine learning approaches.

Keywords: Deep learning; Confusion matrix; Machine learning; Computer Tomography (CT); Magnetic Resonance Imaging (MRI)

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Copyright

© 2022 Ghodeswar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)