AI-Driven Chest X-Ray Diagnostics Efficient Lung Disease Classification

Lung diseases are diseases of the respiratory system, cause abnormal states of affairs with breathing and oxygen exchange. Common include lung cancer, tuberculosis, COVID19, pneumonia, that all affect lung tissues in different manners. Common symptomatic medical conditions may include shortness of breath, coughing and chest pain, often diagnosed and monitored with medical imaging such as X-rays and CT scans of chest. A hybrid model is used, a combination of the Capsule Neural Network with the VGG19 architecture, for the improvement of classification. The spatial hierarchy capturing nature of the Capsule Network will notably reduce the misclassification by understanding the relationships among other different features. VGG19 enhances feature extraction with deeper analysis on images. This innovative approach not only improves the accuracy of predicting but also facilitates simplification in classification to add more disease classes. The AI-based system proposed here improves upon the strength of Capsule Networks and VGG19 for higher reliability and accuracy in the classification of lung diseases. It enables earlystage diagnosis and takes into account the objective of efficiently capturing relationships among images. It supports multi-disease detection and finally overcomes the limitations of different methods used in the field of medical images.