Classification of Musculoskeletal Abnormalities with Convolutional Neural Networks - Sato, Guilherme Tiaki Sassai; da Silva Segundo, Leodécio Braz; Dias, Zanoni (Anais do Simpósio Brasileiro de Bioinformática (BSB); 2020: Anais do XIII Simpósio Brasileiro de Bioinformática, 2020)
Computer-aided diagnosis has the potential to alleviate the burden on medical doctors and decrease misdiagnosis, but building a successful method for automatic classification is challenging due to insuﬃcient labeled data. In this work, we investigate the usage of convolutional neural networks to diagnose musculoskeletal abnormalities using radiographs (X-rays) of the upper limb and measure the impact of several techniques in our model. We achieved the best results by utilizing an ensemble model that employs a support vector machine to combine different models, resulting in an overall AUC ROC of 0.8791 and Kappa of 0.6724 when evaluated using an independent test set.