Advanced CNN based on genetic algorithm to automated femoral neck fracture classification

In this study, we propose an efficient fusion framework that utilizes deep learning and a genetic algorithm for the classification of femoral neck fracture images. This is the first study to utilize a genetic algorithm (GA) to optimize the architecture of a Convolutional neural network (CNN) model for the classification of femoral neck fractures. The proposed CNN was trained on a large dataset of 10 000 real patient cases, who underwent both skeletal bone mineral density measurement and hip X-ray at the University Hospital Center of Oujda between 2016 and 2023. The performance of the model was extensively evaluated and compared to various machine learning and deep learning models, including Random Forest, SVM, VGG19, ResNet50, InceptionV3, and EfficientNet. The experimental results demonstrate that the proposed CNN achieved an accuracy of 97%, and it is currently being used by seven doctors at the University Hospital Center of Oujda, Marocco.