Abstract:
"This thesis addresses the imperative challenge of improving medical diagnostics, focusing on Bone Fracture Detection in X-ray images. The first paragraph introduces the problem, highlighting the limitations of conventional methods and the need for heightened accuracy and efficiency.
To tackle this, the research integrates an autoencoder based anomaly detection system and Convolutional Neural Networks (CNNs) for a comprehensive approach to medical image analysis. To solve the problem, the methodology encompasses the use of autoencoders to extract latent features from bone X-ray images, facilitating anomaly detection based on deviations in reconstructed patterns. Concurrently, a CNN is employed for precise classification of bone fractures. The implementation of advanced image preprocessing methods is pivotal in enhancing overall system performance and reliability.
The initial results of the prototype exhibit promising outcomes. The integrated system effectively identifies anomalies beyond fractures, showcasing its versatility in medical imaging applications. This early success validates the proposed methodology's efficacy and sets the stage for further refinement and optimization. The thesis establishes a foundation for an advanced medical imaging system, offering a transformative approach to Bone Fracture Detection with implications for enhanced diagnostic accuracy in clinical practice.
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