Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia

Abstract

As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient’s X-ray chest radiograph by an experienced doctor takes about 5 to 15 minutes. When cases are concentrated, this will undoubtedly put tremendous pressure on the doctor’s clinical diagnosis. Therefore, relying on the naked eye of the imaging doctor has very low efficiency. Hence, the use of artificial intelligence for clinical image diagnosis of pneumonia is a necessary thing. In addition, artificial intelligence recognition is very fast, and the convolutional neural networks (CNNs) have achieved better performance than human beings in terms of image identification. Therefore, we used the dataset which has chest X-ray images for classification made available by Kaggle with a total of 5216 train and 624 test images, with 2 classes as normal and pneumonia. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results, from which we improved MobileNet’s network structure and achieved a higher accuracy rate than other methods. Furthermore, the improved MobileNet’s network could also extend to other areas for application.

Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis

“…Sirazitdinov et al use an ensemble of RetinaNet and Mask R-CNN for pneumonitis detection and localization [ 12 ]. Yue et al use the Kaggle chest X-ray dataset to perform pneumonitis classification using MobileNet along with other architectures by training for 20 epochs [ 13 ]. Elshennawy and Ibrahim also report a good accuracy with MobileNet and ResNet models when the entire network was retrained [ 14 ].…”

Deep Learning for Chest X-ray Analysis: A Survey

“…Following this, pneumonia is second most studied subject (26 studies). Of the 26 studies that worked with pneumonia, 12 studied pediatric chest X-rays and 11 of those used the Ped-Pneumonia dataset for training and evaluation (Rajaraman et al, 2018a;Yue et al, 2020;Liang and Zheng, 2020;Behzadikhormouji et al, 2020;Elshennawy and Ibrahim, 2020;Ureta et al, 2020;Mittal et al, 2020;Shah et al, 2020;Qu et al, 2020;Ferreira et al, 2020;Anand et al, 2020). 4 and listed in Table 2.…”

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data

“…Artificial Intelligence (AI) approaches have been used intensively in medical domain to diagnose diseases based on the chest scans, e.g. Pneumonia [ [21] , [22] , [23] , [24] , [25] ]. Recognition techniques used range from Bayesian to Deep Learning (DL).…”