![normal hip xray femoral neck fracture xray normal hip xray femoral neck fracture xray](https://upload.orthobullets.com/topic/1038/images/screen_shot_2016-07-19_at_3.17.44_pm.jpg)
Early diagnosis and treatment not only facilitate the protection of the joint, but also help patients to sustain their quality of life and ambulation capacity in the postoperative period. Incidence is predicted to double in the next 30 years, in parallel with the aging global population. The genetic algorithm (GA) approach was employed to optimize the hyperparameters of the CNN architecture and to minimize the error after testing the model created by the CNN architecture in the training phase.įemoral fractures are a major health issue faced by the elderly population. The training process was repeated for pixel sizes 50x50, 100x100, 200x200, and 400x400. To reduce overfitting, regularization term was added to the weights of the loss function. Learning rate was dropped by a factor of 0.5 on every five epochs.
![normal hip xray femoral neck fracture xray normal hip xray femoral neck fracture xray](https://coreem.net/content/uploads/2017/10/Right-Subcapital-Fracture.jpeg)
The training process was terminated after 50 epochs and an Adam Optimizer was used. The last three layers of the architecture were a fully connected layer of two classes, a softmax layer and a classification layer that computes cross entropy loss. After the last block, a dropout layer existed with a probability of 0.5. The proposed convolutional neural network (CNN) architecture contained five blocks, each containing a convolutional layer, batch normalization layer, rectified linear unit, and maximum pooling layer. A total of 1,341 images were fractured femoral necks while 765 were non-fractured ones. A total of 234 frontal pelvic X-ray images collected from 65 patients (32 males, 33 females mean age 74.9 years range, 33 to 89 years) were augmented to 2106 images to achieve a satisfactory dataset. This retrospective study was conducted between January 2013 and January 2018.