DEEP LEARNING FRAMEWORK FOR THE REAL-TIME INTEGRATION OF MULTICLASS LUNG INFECTION DETECTION AND CLASSIFICATION FROM CHEST X-RAY IMAGING

Abstract— This paper presents a complex deep learning- based technique for identifying lunginfections using X-ray pictures of the chest, therefore enabling exact and effective medical diagnostics.Three primary phases of the operation of the system are training, testing, and categorization. Preprocessedchest X-ray images are fed to a strong feature extraction module grounded on convolutionalneural network (CNN) architectures such ResNet or VGG in the training phase. This module usesconvolutional layers, max pooling, residual blocks, and global average pooling to extract fine-grainedand structural elements from the images and let the model discover subtle patterns vital for accuratediagnosis. Image normalisation and noise reduction are among sophisticated pre-processingtechniques that ensure dependable model performance on a range of datasets even more by improvinginput quality. During testing and classification, unseen X-ray images are handled through the samepipeline to guarantee consistency. Optimized feature representations help to classify images intomultiple groups: Viral pneumonia, bacterial pneumonia, and COVID-19, TB, and typical cases. Thesystem's use of explainability tools such as Grad-CAM to show decision-making helps clinicians tobuild trust. Designed with real-world use in mind, this system's practical value rises with supportingreal-time deployment via edge devices or cloud-based APIs. The proposed system combines modernCNN architectures, multi-class classification, explainability, and simplicity to provide a scalable anddependable approach to improve diagnostic procedures and forward healthcare delivery. Keywords— Chest X-ray analysis, lung infection diagnosis, deep learning, Convolutional neuralnetworks, diagnostics in medicine.