In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.

类比比例是形式“A的陈述，如C为D”。它们构成了一个推理工具，提供了一个逻辑框架来解决学习，转移和解释性问题，并且在人工智能和自然语言处理中找到有用的应用。在本文中，我们解决了两个问题，即类别，在形态学中的类比检测和分辨率。多种象征方法解决形态学的类比问题，实现竞争性能。我们表明可以使用数据驱动的策略来胜过这些模型。我们提出了一种利用深度学习来检测和解决形态类别的方法。它编码了类似实物比例的结构性，并依赖于专门设计的嵌入模型捕获词语的形态特征。我们展示了模型对多种语言的类比检测和分辨率的竞争性能。我们提供了分析平衡培训数据的影响，并评估我们对输入扰动的鲁棒性的影响。