The diagnosis of gingivitis often occurs years later using a series of conventional oral examination, and they depended a lot on dental records, which are physically and mentally laborious task for dentists. In this study, our research presented a new method to diagnose gingivitis, which is based on contrast-limited adaptive histogram equalization (CLAHE), gray-level co-occurrence matrix (GLCM), and extreme learning machine (ELM). Our dataset contains 93 images: 58 gingivitis images and 35 healthy control images. The experiments demonstrate that the average sensitivity, specificity, precision, and accuracy of our method is 75%, 73%, 74% and 74%, respectively. This method is more accurate and sensitive than three state-of-the-art approaches.
K E Y W O R D Scontrast-limited adaptive histogram equalization, extreme learning machine, gingivitis, gray-level co-occurrence matrix
This paper presents two new novelty discrimination models for uncorrelated patterns based on neural modelling. The first model uses a single neuron with Hebbian learning and works well when the number of memorised patterns is less than 0.046N (N, the number of inputs). The second model is based on checking the value of the energy function of a Hopfield network. By sacrificing the ability to extract patterns-not needed for familiarity detection-an amazing jump from the normal capacity for retrieval of 0.145N to a capacity for novelty discrimination of 0.023N 2 is achieved. In addition, both models give some insight on the effect of déjà vu, since there is always a very small probability of detecting novel patterns as familiar.
The edge detection-based has profoundly inspired recent works in image classification, object detection, segmentation, et al. With the growth of computer vision, the performance of edge detection has been notably improved. In this paper, we concentrate on presenting some edge detection technologies and grouping them into two major categories: classical edge detection technology and deep learning-based edge detection technology. For every team in the classification, the fundamental thoughts are first described, and then we probe beneath into the mainly related literature in recent years. Furthermore, the evaluation indicators are entirely elaborated. In addition, this conclusion is given by a comprehensive summary which provides suggestions on future perspectives.
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