Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.
Recent advances in Computer Vision and Artificial Intelligence have brought the opportunity to automate facial attractiveness evaluation. A range of studies have been addressed to the task and have achieved reasonable prediction accuracy. However, most of these methods work well only on photos with restrictions on expression, posture, illumination, but not on real-world face photos. This work is aimed to improve the attractiveness assessment state-of-the-art in both cases. To this end, an approach that employs transfer learning methodology as well as shallow machine learning was proposed for highly accurate facial attractiveness prediction. Specifically, a Convolutional Neural Network (CNN), Facenet, originally designed and pre-trained for the face recognition task is utilized. High-level facial features were extracted by using the network and then fed into Support Vector Regression in order to predict facial attractiveness. Extensive experiments conducted on widely used facial beauty datasets Gray and SCUT-FBP5500 demonstrated that the proposed method outperformed other attractiveness prediction approaches. The experimental results also confirmed the effectiveness of the method in both constrained and unconstrained environment.
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