Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Automated Medical Image Classification for Disease Prognosis

Authors
R. Tamilkodi1, *, V. Bala Sankar2, D. Bhoomika Chowdary3, D. Teja Usha Devi3, D. Satya Devi3, J. Sanjana3, V. Mrudula3
1Professor, Godavari Institute of Engineering & Technology, Rajahmundry, Andhra Pradesh, India
2Assistant Professor, Department of CSE (AIML & CS) Godavari Institute of Engineering & Technology, Rajahmundry, Andhra Pradesh, India
3Department of Computer Science & Engineering (AIML & CS) Godavari Institute of Engineering & Technology, Rajahmundry, Andhra Pradesh, India
*Corresponding author. Email: tamil@giet.ac.in
Corresponding Author
R. Tamilkodi
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_127How to use a DOI?
Keywords
Medical Image Classification; Pretrained CNN; DCNN; ResNet-50
Abstract

An interesting exploration challenge for computer vision experimenters is the automated classification of medical pictures, made possible by recent advances in imaging technology. Medical pictures need to be sorted into their proper categories, and a good classifier is essential for this. With our proposed approach, the system would be pre-trained to recognize and categorize medical pictures using deep learning techniques such as GoogLeNet, VGG-16 and ResNet-50 these three pre-trained deep convolutional neural networks are utilized for categorizing the different medical pictures. Using this picture bracketing approach to predict the appropriate category or sequence of unidentified photographs may be quite helpful. The findings of the experiment demonstrated with the standard dataset in which the proposed method is superior at categorizing different types of medical pictures.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_127
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_127How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - R. Tamilkodi
AU  - V. Bala Sankar
AU  - D. Bhoomika Chowdary
AU  - D. Teja Usha Devi
AU  - D. Satya Devi
AU  - J. Sanjana
AU  - V. Mrudula
PY  - 2024
DA  - 2024/07/30
TI  - Automated Medical Image Classification for Disease Prognosis
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1325
EP  - 1334
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_127
DO  - 10.2991/978-94-6463-471-6_127
ID  - Tamilkodi2024
ER  -