부산대학교 도서관

Pneumonia is an infectious and deadly disease. According to the World Health Organization (WHO), every third person dies due to this disease. It can be cured if detected accurately and on time. Chest X-rays are used to diagnose this disease, but it requires expert radiotherapists and a very time-consuming process. So, it is the need of the hour to develop an automatic system to detect pneumonia that could perform better and produce faster results. However, traditional handcrafted machine learning techniques show low accuracy and are expensive in terms of complexity. Deep convolutional neural networks (D-CNNs) show better performance in this regard and are simple and easy to use as compared to machine learning algorithms. In this paper, a novel algorithm based on AlexNet and SVM is proposed to detect pneumonia. We also compared the results of AlexNet with other D-CNNs to check which one is performing better. Experimental results prove that AlexNet integrated with SVM outperforms all other techniques.