Fusing Deep Learning with Support Vector Machines to Detect COVID-19 in X-Ray Images

Deep neural networks are powerful learning machines that have laid foundations for most of the recent advancements in data analysis. Their most important advantage lies in learning how to extract the features from raw data, and these deep features are later classified with fully-connected layers. Although there exist more effective classifiers, including support vector machines, their high computational complexity is a serious obstacle in using them for classifying highly-dimensional and often huge datasets of deep features. We introduce a new framework which allows us to classify the deep features with evolutionarily-optimized support vector machines and we apply it to a real-life problem of detecting COVID-19 from X-ray images. We demonstrate that the proposed approach is highly effective and it outperforms well-established transfer learning strategies, thus improving the potential of existing pre-trained deep models. It can be particularly beneficial in cases when the amount and quality of labeled data is insufficient for performing full training of a network, but still too large for training a regular support vector machine.