Bone Fracture Detection Using Convolutional Neural Networks

Registration ID: IJNRD_219565

Published ID: IJNRD2404784

DOI: Click Here to Get

Arnim Roopansh Goyal , Ansh Mehta , Harsh Goel

Bone fractures are the major and common issues faced by many people. These fractures often occur during accidents. To predict these fractures doctors are using x-rays. Sometimes it is difficult to predict whether it is fractured or not through the xrays manually. These x-rays show a clear picture of the damage but the main issue is that some physicians are overlooking the small fractures which may cause a lot of damage in the future to that particular person. Model which analyses and classifies the images of hand, leg, chest, fingers and wrist fractures in a clear way. There are many other techniques to detect these fractures and this project is molded by using some artificial intelligence applications using machine learning and deep learning techniques. This project investigates specifically various models dependent on Convolutional Neural Networks which helps us to provide a better solution as it is a step-by-step process of image analyzing algorithm to predict whether the bone is fractured or normal. By comparing 3 types of CNN models which are ConvNet/CNN, VGG16 & R-CNN with the same image dataset, R-CNN gave the best accuracy.

Published Paper   E-Certificate

Arnim Roopansh Goyal, Ansh Mehta, & Harsh Goel (April-2024). Bone Fracture Detection Using Convolutional Neural Networks. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h765-h774. https://ijnrd.org/papers/IJNRD2404784.pdf

Volume 9 Issue 4, April-2024

Pages : h765-h774

Paper Reg. ID: IJNRD_219565

Published Paper Id: IJNRD2404784

Downloads: 000121983

Research Area: Engineering

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404784.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404784

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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