In the domain of clinical imaging, the exact and quick identification proof of bone fractures assumes a crucial part in a pivotal role in facilitating timely and effective patient care. This research tends to this basic need by harnessing the force of profound learning, explicitly utilizing a Convolutional Neural Network (CNN) model as the foundation of our technique. The essential target of our study was to improve the mechanized recognition of bone fractures in X-ray images, utilizing the capacities of deep learning algorithms. The use of a CNN model permitted us to successfully capture and learn intricate patterns and features within the X-ray images, empowering the framework to make exact fracture detections. The training process included presenting the model to a various dataset, guaranteeing its versatility to an extensive variety of fracture types. The results of our research show the excellent performance of the CNN model in fracture detection, where our model has achieved an Average Precision 89.5%, Average Recall 87%, and the overall Accuracy 91%. These metrics assert the vigour of our methodology and highlight the capability of deep learning in medical image analysis.
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Tropical cyclones (TC) are among the worst natural disasters, that cause massive damage to property and lives. The meteorologists track these natural phenomena using Satellite imagery. The spiral rain bands appear in a cyclic pattern with an eye as a center in the satellite image. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. Conventional approaches use only image data to detect the cyclic structure using deep learning algorithms. The training and testing data consist of positive and negative samples of TC. But the cyclic structure’s texture pattern makes it difficult for the deep learning algorithms to extract useful features. This paper presents an automatic TC detection algorithm using optical flow estimation and deep learning algorithms to overcome this draw-back. The optical flow vectors are estimated using the Horn-Schunck estimator, the Liu-Shen estimator, and the Lagrange multiplier. The deep learning algorithms take the optical flow vectors as input during the training stage and extract the features to identify the cyclone’s circular pattern. The software used for experimental analysis is MATLAB 2021a. The proposed method increases the accuracy of detecting the cyclone pattern through optical flow vectors compared to using the pixel intensity values. By using proposed method 98% of accuracy will be achieved when compared with the existing methods.
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