Publisher

IAESCOREIAESCORE

Journal International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE) Abstract

Many people suffer from bone fractures, which can result from minor accidents, forceful blows, or even diseases like osteoporosis or bone cancer. In the medical realm, accurately identifying bone fractures from X-ray images is paramount for effective diagnosis and treatment. To address this, a comparative study is conducted utilizing three distinct models: a traditional convolutional neural network (CNN), MobileNet-V2, and a newly developed parallel deep convolutional neural network (PDCNN). The primary aim is to evaluate and contrast these models in terms of precision, sensitivity, and specificity for diagnosing bone fractures. X-ray images of fractured and non-fractured bones are sourced from Kaggle and subjected to various image processing techniques to rectify anomalies. Techniques such as cropping, resizing, contrast enhancement, filtering, and augmentation are applied, culminating in canny edge detection. These processed images are then used to train and test models. The results showcased the superior performance of the newly developed PDCNN model, achieving an impressive accuracy of 92.89%, surpassing both the traditional CNN and pretrained MobileNet-V2 models. A series of ablation studies are conducted to fine-tune the hyperparameters of the PDCNN model, further validating its efficacy. Throughout the investigation, PDCNN consistently outperformed MobileNet-V2 and traditional CNN, underscoring its potential as an advanced tool for streamlining bone fracture identification.

Conclusion

The study demonstrates the effectiveness of the proposed PDCNN model in accurately detecting bone fractures from X-ray images, surpassing traditional CNN and MobileNet-V2 models.Rigorous validation through ablation studies confirms the models robustness and optimal performance.These findings contribute to advancements in medical image analysis and have the potential to improve diagnostic accuracy and efficiency in clinical settings.

Future Research

Future research should explore the integration of PDCNN with other imaging modalities, such as CT scans and MRI, to enhance diagnostic capabilities and provide a more comprehensive assessment of bone fractures. Investigating the application of PDCNN in detecting subtle or complex fracture patterns, which are often challenging for human radiologists, could further improve diagnostic accuracy. Additionally, developing a user-friendly interface and deploying PDCNN as a clinical decision support system could facilitate its widespread adoption and integration into routine medical practice, ultimately benefiting patients and healthcare professionals alike. These advancements will require further data collection, model refinement, and clinical validation to ensure the reliability and effectiveness of PDCNN in real-world healthcare settings.

References

1. Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images | Bitto... doi.org/10.26555/ijain.v9i2.872Tumor Net convolutional neural network modeling for classifying brain tumors from MRI images Bitto doi 10 26555 ijain v9i2 872

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