A New Benchmark on Musculoskeletal Abnormality Recognition System using Deep Transfer Learning Model

Musculoskeletal disorders are abnormalities of the bones, muscles, and joints that affect the great majority of people worldwide. Radiographic scans are the most widely used technique for detecting these aberrations as part of medical diagnostics. Early-stage detection of anomalies in radiographs is crucial for the patient. Moreover, bone fracture classification is costly, more time-consuming, and requires more effort. These reasons have made the deep learning-based classifier model a reliable alternative. In this study, we have applied a deep transfer learning technique to the problem of classifying such anomalies in radiographs of the musculoskeletal system. For multiclass recognition of radiological images into seven categories, the suggested model has been executed on the widely used MURA dataset, which includes 40,561 radiograph images. We utilized the DenseNet121 pre-trained model with some optimization in the suggested model. Our proposed model achieved the highest accuracy of $96.62 \%$. The new model achieved a precision of $96.73 \%$, a recall of $96.62 \%$, an $F 1$-score of $96.60 \%$, and a Cohen Kappa score of $96.06 \%$. We attain better performances that approximately $3 \%$ exceed the previous study and are also comparable to state-of-the-art performances.