A novel and ultralight convolutional neural network model for real-time detection of infectious lung diseases.

Objective: Vectors that cause infectious lung diseases encompass viral, bacterial, fungal, and parasitic agents. Early detection of these infections is critical for timely diagnosis and effective treatment. Several studies have created solutions for early detection with varying performance, but with limitations such as image type specificity, lack of generalizability, potential overfitting, and bias problems. Our model effectively addresses these problems by using diverse image types, enhancing robustness, and generalizability across various contexts that aim for effective performance. Methods: This study creates an early detection model that works with both CT scans and X-ray images. We applied a convolutional neural network (CNN) model trained on diverse and large augmented datasets with fewer parameters. We then used a generative adversarial network (GAN) to validate our CNN model and create generalized synthetic images. The proposed model was trained primarily on COVID-19, pneumonia, and tuberculosis (TB) cases (n = 432,533 total augmented cases). Results: The proposed model is a lightweight and explainable model that assists with real-time detection, resulting in a better performance with an average accuracy of 97.93% with a standard deviation of 0.97%, average area under the curve (AUC) of 98.07%, average sensitivity of 98.46%, average specificity of 97.03%, average precision of 97.45%, and average F1 score of 97.95%. Conclusion: The proposed CNN model offers a validation and generalization capability for diverse image types in real-time. We conducted a comparative analysis of our model with the most advanced research. The integration of our approach with other clinical systems and internet of things (IoT) devices is feasible.