RESUMO
This paper introduces a solution to address the intricacy of the model employed in the deep learning-based diagnosis of musculoskeletal abnormalities and the limitations observed in the performance of a single deep learning network model. The proposed approach involves the integration of an improved EfficientNet-B2 model with MobileNetV2, resulting in the creation of FusionNet. First, EfficientNet-B2 is combined with coordinate attention (CA) to obtain CA-EfficientNet-B2. Furthermore, aiming to minimize the model parameter count, we further enhanced the mobile inverted residual bottleneck convolution module (MBConv) employed for feature extraction in EfficientNet-B2, resulting in the development of CA-MBC-EfficientNet-B2. Next, the features extracted from CA-MBC-EfficientNet-B2 and MobileNetV2 are fused. Finally, the final diagnosis of musculoskeletal abnormalities was performed by using fully connected layers. The experimental results demonstrate that, first, compared to EfficientNet-B2, CA-MBC-EfficientNet-B2 not only significantly improves the diagnostic performance of musculoskeletal abnormalities, it also reduces the parameter count and storage space by 17%. Moreover, as compared to other models, FusionNet demonstrates remarkable performance in the area of anomaly diagnosis, particularly on the elbow dataset, achieving a precision of 92.93%, an AUC of 93.89% and an accuracy of 87.10%.
