Osteochondroma Identification Through Transfer Learning and Convolutional Neural Networks

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.