Recent developments in the field of machine learning have led to drastic improvements in medical diagnosis. Identification of different medical conditions with high accuracy is possible through machine learning, specifically deep learning. Convolutional Neural Networks are a subset of deep neural networks, used in investigating visual images.

In this study, a method to identify bacterial pneumonia, viral pneumonia and COVID-19 from chest X-rays is proposed using convolutional neural networks. Training accuracy of 0.9440 and validation accuracy of 0.9356 was obtained using this model. The test accuracy was found to be 0.8753. As a matter of fact, COVID-19 diagnosing precision and recall of the proposed method are 0.95 and 1.00 respectively. Significant improvements are seen when compared to other approaches.

机器学习领域的最新发展已导致医学诊断的巨大进步。通过机器学习，特别是深度学习，可以高精度地识别不同的医疗状况。卷积神经网络是深度神经网络的子集，用于研究视觉图像。

在这项研究中，提出了一种使用卷积神经网络从胸部X光片中识别细菌性肺炎，病毒性肺炎和COVID-19的方法。使用该模型获得了0.9440的训练精度和0.9356的验证精度。测试准确性为0.8753。实际上，COVID-19的诊断精度和召回率分别为0.95和1.00。与其他方法相比，可以看到显着的改进。