Research Article
X-ray body Part Classification Using Custom CNN
@ARTICLE{10.4108/eetpht.10.5577, author={Reeja S R and Sangameswar J and Solomon Joseph Joju and Mrudhul Reddy Gangula and Sujith S}, title={X-ray body Part Classification Using Custom CNN}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={Analyze x-ray images, CNN, Classification of x-ray body parts}, doi={10.4108/eetpht.10.5577} }- Reeja S R
Sangameswar J
Solomon Joseph Joju
Mrudhul Reddy Gangula
Sujith S
Year: 2024
X-ray body Part Classification Using Custom CNN
PHAT
EAI
DOI: 10.4108/eetpht.10.5577
Abstract
INTRODUCTION: This work represents a significant step forward by harnessing the power of deep learning to classify X-ray images into distinct body parts. Over the years X-ray pictures were evaluated manually. OBJECTIVE: Our aim is to automate X-ray interpretation using deep learning techniques. METHOD: Leveraging cutting-edge frameworks such as FastAI and TensorFlow, a Convolutional Neural Network (CNN) has been meticulously trained on a dataset comprising DICOM images and their corresponding labels. RESULT: The results achieved by the model are indeed promising, as it demonstrates a remarkable ability to accurately identify various body parts. CNN shows 97.38% performance by compared with other classifiers. CONCLUSION: This innovation holds the potential to revolutionize medical diagnosis and treatment planning through the automation of image analysis, marking a substantial leap forward in the field of healthcare technology.
Copyright © 2024 Reeja S. R. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
