Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

• Published in: Healthcare (Basel) Vol. 11; no. 11; p. 1561
• Main Authors: Reshan, Mana Saleh Al, Gill, Kanwarpartap Singh, Anand, Vatsala, Gupta, Sheifali, Alshahrani, Hani, Sulaiman, Adel, Shaikh, Asadullah
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.05.2023; MDPI
• Subjects: Accuracy; Algorithms; Bacterial pneumonia; Classification; Clinical outcomes; Coronaviruses; COVID-19; Datasets; Diagnosis; Lung diseases; Lungs; Machine learning; Medical imaging; Methods; Patients; Pneumonia; X-rays; Saudi Arabia; deep learning; transfer learning; chest X-ray images; disease; pneumonia; classification
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare11111561

Pneumonia has been directly responsible for a huge number of deaths all across the globe. Pneumonia shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in the way chest X-ray images are acquired and processed, which can impact the quality and consistency of the images. This can make it challenging to develop robust algorithms that can accurately identify pneumonia in all types of images. Hence, there is a need to develop robust, data-driven algorithms that are trained on large, high-quality datasets and validated using a range of imaging techniques and expert radiological analysis. In this research, a deep-learning-based model is demonstrated for differentiating between normal and severe cases of pneumonia. This complete proposed system has a total of eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet. These eight pre-trained models were simulated on two datasets having 5856 images and 112,120 images of chest X-rays. The best accuracy is obtained on the MobileNet model with values of 94.23% and 93.75% on two different datasets. Key hyperparameters including batch sizes, number of epochs, and different optimizers have all been considered during comparative interpretation of these models to determine the most appropriate model.