| Abstrakt: |
The human upper extremity, which includes the shoulder, humerus, elbow, forearm, wrist, hand, and fingers, demonstrates incredible biological complexity, enabling us to make crucial and intricate movements in our daily lives. Due to its delicate construction, it is prone to a variety of fractures and injuries. Radiologists utilize a combination of visual perception, memory, pattern recognition, and cognitive reasoning when interpreting radiographic studies. However, a variety of circumstances, including clinical staff shortages, a lack of skilled radiologists, heavy caseloads, and continual distractions, can all hurt their performance, resulting in higher workloads, fatigue, wrong detection rates, and weariness. Addressing these challenges, our study introduced a novel approach employing an advanced deep learning model to enhance the accuracy and efficiency of diagnosing finger radiograph abnormalities. In this work, a modified DenseNet hybrid architecture was proposed for classifying abnormalities in finger radiographs. These models were trained using the finger dataset, a subset of the extensive Musculoskeletal Radiographs (MURA) dataset. The improved performance indicators for our proposed model over the current benchmark models demonstrated the model's effectiveness. The proposed models exhibited superior performance in Cohen's kappa metrics for classifying abnormalities in finger radiographs on the test dataset, outperforming other benchmark models. The advanced deep learning model based on DenseNet architecture showcased the promising potential for automating the classification of abnormalities in finger radiographs. |
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