This project enhances medical diagnostic accuracy by developing a deep learning model that detects cervical fractures from vertebral images. Using a Convolutional Neural Network (CNN) with the EfficientNet architecture in TensorFlow and integrating YOLOv5 for object detection, this model achieves high accuracy and is a valuable tool for medical professionals.
- EfficientNet Architecture: Implements a CNN using EfficientNet to achieve a 95.2% classification accuracy rate for vertebral images.
- YOLOv5 Object Detection: Integrates YOLOv5 to advance fracture detection capabilities, attaining an 86% accuracy rate.
- Deep Learning in Medical Image Analysis: Utilizes state-of-the-art deep learning techniques to improve the accuracy and efficiency of cervical fracture detection.
