Abnormality Detection from X-Ray Bone Images using DenseNet Convolutional Neural Network

Shukla Abhilash, Patel Atul
2021 International Journal of Current Research and Review  
According to the survey of the World Health Organization and the International Agency for Research on Cancer; the death rate because of cancer is increasing day by day. It is preferable to detect cancer at its earlier stage or detect any kind of lesion which can cause cancer in the future. This paper shows how Artificial Intelligence especially the Convolutional Neural Network of Deep Learning can be used to detect abnormality from X-Ray bone images. Objective: To detect the abnormality in bone
more » ... from X-Ray Images. Methods: MURA (Musculoskeletal Radiographs) dataset is used which was prepared by the Stanford ML group. Dataset is identified and categorized into training and validation dataset and after that data preprocessing techniques are used. This help in making the dataset convenient for the DenseNet (Densely Connected Convolutional Networks) Model. Tenserflow and Keras libraries are used to build DenseNet model. Table and Confusion Matrix methods are used to evaluate the performance of DenseNet (Densely Connected Convolutional Networks) Model for the detection of abnormality in bone from X-Ray Images. By using this proposed model more than 85% accuracy achieved. Results: Classification Conclusion: The result obtains from the proposed model will be helpful to the radiologist to make better decisions. This independent model can further be used to detect cancer of bone from X-Ray Images.
doi:10.31782/ijcrr.2021.131026 fatcat:3tufrrm3xzffne52mrq3agb2nu