X-ray body Part Classification Using Custom CNN

Reeja S R; Sangameswar J; Solomon Joseph Joju; Mrudhul Reddy Gangula; Sujith S

doi:10.4108/eetpht.10.5577

Authors

Reeja S R Vellore Institute of Technology University
Sangameswar J Vellore Institute of Technology University
Solomon Joseph Joju Vellore Institute of Technology University
Mrudhul Reddy Gangula Vellore Institute of Technology University
Sujith S Vellore Institute of Technology University

DOI:

https://doi.org/10.4108/eetpht.10.5577

Keywords:

Analyze x-ray images, CNN, Classification of x-ray body parts

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.

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