Deep Learning-based Chest X-Ray Analysis for Early Lung Disease Detection

Abstract

Pulmonary conditions, like lung cancer, tuberculosis, covid-19, pneumonia (bacterial and viral) and other conditions, like normal lungs, impact the respiratory system, respiratory disorders and oxygen metabolism. Pulmonary conditions can lead to symptoms like cough, shortness of breath and chest pain, usually necessitating medical visualization (X -ray of chest and computed tomography) for diagnosis and follow-up. To enhance lung disease categorization, a hybrid AI model that integrates Capsule Neural Networks (Caps Net) and the VGG19 architecture is suggested. Caps Net is used to extract spatial hierarchies in images, realizing the interrelation between various features and minimizing the chances of misclassification. This is especially beneficial in medical visualization where image interrelation is crucial for proper diagnosis. A profound, sticky neural network, VGG19 boosts sine extraction and enables the model to inspect images more intensely. This is a combination of the two models' strength in order to ease the process of classification and enhances the accuracy of prediction, enabling more types of diseases like bacterial pneumonia, viral pneumonia, and crown viruses to be covered. Computer algorithms give a safer and more exact approach to diagnosing lung illness, particularly early on. Notebooks are complex relationships of pictures, with capabilities for multi-bourton recognition and consideration for the limitations of present medical imagery techniques.