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Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model
Mana Saleh Al Reshan1, Kanwarpartap Singh Gill2, Vatsala Anand2
1Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Abstract:
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.
Insights
A deep learning model effectively differentiates normal from severe pneumonia using chest X-rays. The MobileNet model achieved the highest accuracy, demonstrating its potential for improved pneumonia diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Science
Background:
- Pneumonia is a leading global cause of death, often visually similar to other respiratory diseases like tuberculosis.
- Variability in chest X-ray acquisition and processing complicates accurate pneumonia detection.
- Developing robust, data-driven algorithms for pneumonia identification is crucial.
Purpose of the Study:
- To develop and validate a deep-learning model for distinguishing between normal and severe pneumonia cases using chest X-ray images.
- To compare the performance of eight pre-trained deep learning models for pneumonia classification.
Main Methods:
- A deep-learning system was designed, incorporating eight pre-trained models: ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet.
- These models were trained and evaluated on two distinct chest X-ray datasets (5,856 and 112,120 images).
- Hyperparameter tuning, including batch sizes, epochs, and optimizers, was performed for model optimization.
Main Results:
- The MobileNet model demonstrated the highest classification accuracy, achieving 94.23% on one dataset and 93.75% on the other.
- Comparative analysis identified MobileNet as the most effective model among the eight tested.
- The study systematically evaluated model performance based on key hyperparameters.
Conclusions:
- Deep learning models, particularly MobileNet, show significant promise for accurate pneumonia detection from chest X-rays.
- The findings highlight the potential of AI in improving the diagnosis of pneumonia, addressing challenges posed by image variability.
- Further validation using diverse imaging techniques and expert analysis is recommended.
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