Osteochondroma Identification Through Transfer Learning and Convolutional Neural Networks

Ayesha Afridi ()
Additional contact information
Ayesha Afridi: NFC Institute of Engineering and Technology Multan, 60000, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 2, 608-620

Abstract: Accurate and timely diagnosis of musculoskeletal conditions like osteochondroma is pivotal in ensuring effective treatment and improved patient outcomes. However, traditional diagnostic methods relying on manual interpretation of medical images can be susceptible to human errors, potentially leading to misdiagnosis or delayed detection. Previous studies have explored Deep Learning (DL) techniques for automated disease detection, but they often face challenges such as limited dataset availability and generalization capabilities across diverse imaging modalities. This research addresses these gaps by proposing a robust Convolutional Neural Network (CNN) framework for osteochondroma identification, leveraging transfer learning and data augmentation techniques. The ResNet-50 architecture, pre-trained on a large dataset, is fine-tuned with dense layers and an output layer for binary classification. Extensive data pre-processing and offline augmentation strategies enhance model performance and generalizability. The proposed model achieves an impressive 97.67% accuracy on the test dataset, demonstrating its effectiveness in distinguishing between normal and osteochondroma cases. Furthermore, its generalizability is validated by training and testing on the publicly available Potato Leaf Disease dataset, showcasing consistent performance in multi-class classification scenarios. While the model exhibits promising results, future work could explore integrating more extensive and diverse datasets and investigating advanced architectures for improved accuracy and computational efficiency. The implications of this research extend to empowering medical practitioners with accurate and swift osteochondroma diagnostics, ultimately contributing to enhanced patient care in orthopaedics.

Keywords: Osteochondroma; Deep Learning; Convolutional Neural Networks; Transfer Learning; Medical Imaging, Image Classification (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/814/1397 (application/pdf)
http://journal.50sea.com/index.php/IJIST/article/view/814 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:2:p:608-620

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().