An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

R Vasudeva; S N Chandrashekara

doi:10.17485/IJST/v16i35.945

Article

An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i35.945

Year: 2023, Volume: 16, Issue: 35, Pages: 2796-2806

Original Article

An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

R Vasudeva^1*, S N Chandrashekara²

¹Research Scholar, Department of Computer Science and Engineering, C Byregowda Institute of Technology, Kolar, Visvesvaraya Technological University, Belagavi, 590018, Karnataka, India
²Professor and Head, Department of Computer Science and Engineering, C Byregowda Institute of Technology, Kolar, Visvesvaraya Technological University, Belagavi, 590018, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:23 April 2023, Accepted Date:18 August 2023, Published Date:15 September 2023

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

Abstract

Objectives: The objective of this work is to obtain an efficient medical image retrieval and classification from a larger healthcare datasets using Novel approach. Methods: In this study five different classes of Medical images are taken for input, features are extracted using GLCM (Grey Level Co-occurrence Matrix) by image attributes such as dissimilarity, correlation, homogeneity, contrast, ASM, and energy. The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers. The received feature vectors are input into the most often used deep learning models Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN) for image classification. Then CNN model is integrated with a deep learning model based on Long-Short Term Memory (LSTM), which incorporates additional layers into its structure and works on large datasets. Further the retrieval performance is improved by Euclidean Distance Technique. Findings: Performance evaluation is performed by comparing and analyzing the experimental findings of proposed methods, ANN, CNN and CNN-LSTM yields the retrieval accuracy of 97.79%, 98.78% and 99.4%. The Precision, Recall and F1-Score are also compared, and they are more accurate when picture classification is performed on larger healthcare datasets. Novelty: The additional feature extraction using GLCM and the proposed hybrid model can extract better medical image features, and achieve higher classification accuracy compared with earlier image classification models.

Keywords: ContentBased Image Retrieval; Grey Level Cooccurrence Matrix; Artificial Neural Networks; Convolution Neural Networks; LongShort Term Memory; Cloud Computing

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Copyright

© 2023 Vasudeva & Chandrashekara. 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)