X-Ray bone abnormalities detection using MURA dataset
This work addresses bone fracture and oncology diagnosis for medical specialists, but it is incremental as it applies existing methods to a known dataset.
The paper tackles bone abnormality detection on radiographs using the MURA dataset, achieving Kappa scores of 0.942 on wrist, 0.862 on hand, and 0.735 on shoulder, comparable to state-of-the-art results.
We introduce the deep network trained on the MURA dataset from the Stanford University released in 2017. Our system is able to detect bone abnormalities on the radiographs and visualise such zones. We found that our solution has the accuracy comparable to the best results that have been achieved by other development teams that used MURA dataset, in particular the overall Kappa score that was achieved by our team is about 0.942 on the wrist, 0.862 on the hand and o.735 on the shoulder (compared to the best available results to this moment on the official web-site 0.931, 0.851 and 0.729 accordingly). However, despite the good results there are a lot of directions for the future enhancement of the proposed technology. We see a big potential in the further development computer aided systems (CAD) for the radiographs as the one that will help practical specialists diagnose bone fractures as well as bone oncology cases faster and with the higher accuracy.