Medical & biological engineering & computing
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Med Biol Eng Comput · Apr 2019
A reliable method for colorectal cancer prediction based on feature selection and support vector machine.
Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. ⋯ SVM kernel selection aims to find the best kernel function for classification by comparing Linear, RBF, Sigmoid, and Polynomial kernel types of SVM, and the result shows the best kernel is RBF. Classification performance of LR + RF, LR + NB, LR + KNN, and LR + ANNs models are compared with LR + SVM. After these steps, the cancer and healthy individuals can be classified, and the best model is selected.
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Med Biol Eng Comput · Jan 2019
A novel fused convolutional neural network for biomedical image classification.
With the advent of biomedical imaging technology, the number of captured and stored biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and biomedical institutions. Therefore, more robust biomedical image analysis technology is needed to meet the requirement of the diagnosis and classification of various kinds of diseases using biomedical images. However, the current biomedical image classification methods and general non-biomedical image classifiers cannot extract more compact biomedical image features or capture the tiny differences between similar images with different types of diseases from the same category. ⋯ In addition, we also evaluated the performance of our method in modality classification of medical images using the ImageCLEFmed dataset. Graphical abstract The graphical abstract shows the fused, deep convolutional neural network architecture proposed for biomedical image classification. In the architecture, we can clearly see the feature-fusing process going from shallow layers and the deep layers.
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Med Biol Eng Comput · Dec 2018
Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.
Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. ⋯ Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.
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Med Biol Eng Comput · Oct 2018
Biomechanical features of six design of the delta external fixator for treating Pilon fracture: a finite element study.
Pilon fractures can be caused by high-energy vertical forces which may result in long-term patient immobilization. Many experts in orthopedic surgery recommend the use of a Delta external fixator for type III Pilon fracture treatment. This device can promote immediate healing of fractured bone, minimizing the rate of complications as well as allowing early mobilization. ⋯ The highest equivalent von Mises stress was found at the calcaneus pin of the Delta4 (423.2 MPa) as compared to others. In conclusion, Delta1 external fixator was the most favorable option for type III Pilon fracture treatment. Graphical abstract ᅟ.
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Med Biol Eng Comput · Jul 2018
Classifying clinical notes with pain assessment using machine learning.
Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often suffer from deficits in pain care quality (PCQ) including pain assessment, treatment, and reassessment. Currently, there is no intelligent and reliable approach to identify PCQ indicators inelectronic health records (EHR). ⋯ We developed a Random forest classifier to identify clinical notes with pain assessment information. Compared to other classifiers, ours achieved the best results in most of the reported metrics. Graphical abstract Framework for detecting pain assessment in clinical notes.