Binary Classification of Bone Fractures using Transfer Learning: A Comprehensive Approach

Abstract

In medical imaging, timely and accurate diagnosis of bone fractures is essential. This work offers a thorough approach to binary classification using VGG16 and ResNet architectures in Transfer Learning on X-ray images. In order to maximize model performance, the methodology includes extensive preprocessing steps like data augmentation, resizing, and normalization. The process of extracting features involves honing in on pre-trained models to capture complex fracture patterns. To improve the extracted features, Principal Component Analysis is used, which reduces dimensionality without sacrificing important information. The feature set is then processed by a machine learning classification algorithm, like Random Forest or Support Vector Machines, to enable accurate fracture detection. The efficacy of the suggested method is demonstrated by experimental validation carried out on various X-ray datasets, providing a comprehensive solution for accurate and efficient bone fracture diagnosis in medical imaging.

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