Gender Detection from Spine X-ray Images Using Deep Learning.
Xue Z, Rajaraman S, Long LR, Antani SK, Thoma GR
Proc. IEEE International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. pp. 54-58, DOI:10.1109/CBMS.2018.00017.
Abstract:
The algorithm described in this paper aims to classify the spine x-ray images according to image characteristics that exhibit gender. We developed a customized sequential CNN model which is trained from scratch using the spine images first and tested it on the NHANES II dataset hosted by the U.S. National Library of Medicine (NLM). Aiming to improve the performance, we then developed a method for extracting the region-of-interest (ROI) in the cervical spine images using a content-based image retrieval (CBIR) method and compared the results of using the original images vs. the ROI images. Later, we applied/tested the method of fine-tuning a DenseNet model that was pre-trained with the ImageNet dataset with the spine images, and this approach gets the best result, achieving classification accuracy of 99% for cervical spine image set and 98% for the lumbar spine image set.
Xue Z, Rajaraman S, Long LR, Antani SK, Thoma GR. Gender Detection from Spine X-ray Images Using Deep Learning.
Proc. IEEE International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. pp. 54-58, DOI:10.1109/CBMS.2018.00017.