Bone fracture classification comparision of resnet, densenet, inception V3 with Adam optimizer & mobile net

In the present day, deep learning applications are widely used in every field of development. In this paper, such a type of application is discussed in real-time. Mura dataset is the largest abnormal dataset given by the University of Stanford. In this research paper, we will understand the pre-proc...

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Bibliographic Details
Published in:AIP Conference Proceedings 2024-05, Vol.2900 (1)
Main Authors: Mallam, Pooja, Mehta, Ashu, Singh, Baljeet
Format: Article
Language:English
Subjects:
Accuracy
Datasets
Deep learning
Image classification
Online Access:Get full text
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Summary:In the present day, deep learning applications are widely used in every field of development. In this paper, such a type of application is discussed in real-time. Mura dataset is the largest abnormal dataset given by the University of Stanford. In this research paper, we will understand the pre-processing of the image dataset. Kera’s and TensorFlow libraries are used to build this model. We are classifying images using different ML models where accuracy is compared to get the best model build. This paper gives us a clear understanding of building deep learning models, understanding pre-processing techniques, and working on heavy data. This research’s main agenda is to create a model which can classify bone fracture images and also give a small path for further development in the deep learning area. In this project we are using Resnet50, InceptionV3, Mobile Net and DenseNet with optimizer Adam and comparing their accuracies graphically.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0207516