Osteochondroma Identification Through Transfer Learning and Convolutional Neural Networks
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 2
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, pretrained 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 multiclass 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.
Authors and Affiliations
Ayesha Afridi, Muhammad Kamran Abid, Naeem Aslam, Shumaila Khan, Arsalan Khan, Muhammad Ahmad Nawaz ul Ghani
Keywords
Related Articles
- EP ID EP760330
- DOI -
- Views 27
- Downloads 0
Deep Learning Based Multi Crop Disease Detection System
This research explores the integration of deep learning, computer vision, and edge computing to revolutionize crop disease detection. In response to the pressing need for prompt and accurate disease identification, thi...
Optimizing UAV Wing Performance: A Computational Analysis with Computer-Based Algorithms for Composite Material Integration
Introduction/Importance of Study: The aircraft wing, a vital component, demands intricate design to balance lift generation, drag reduction, and weight minimization. In advanced UAVs (Unmanned Aerial Vehicles), priorit...
Cow Face Detection for Precision Livestock Management using YOLOv8
Precision livestock management is transforming traditional agricultural practices by boosting productivity, increasing yield, and automating tasks, all while reducing labor requirements and minimizing errors. Conventio...
Exploring the Efficacy of CNN Architectures for Esophageal Cancer Classification Using Cell Vizio Images
Esophageal cancer, as with the global burden of disease, is usually due to Barrret's esophagus and gastroesophageal reflux disease. Fortunately, the disease is amenable to early detection; however, early diagnosis has...
Effects of Filters inRetinal Disease Detection onOptical Coherence Tomography (OCT) ImagesUsing Machine Learning Classifiers
Optical Coherence Tomography (OCT) is an essential, non-invasive imaging technique for producing high-resolution images of the retina, crucial in diagnosing and monitoring retinal conditions such as diabet...