X-ray body Part Classification Using Custom CNN

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.