Deep Learning-Based Hybrid Framework For Pneumonia Detection From Chest X-Ray Images with XAI And Real-time Web-interface

The main contributions of this paper
to automated pneumonia screening are:
•A pipeline to preprocess the raw chest X-ray images by
resizing, denoising using Gaussian filter, pixel normalization, and enhancing contrast cleaning up images before
they are fed to any model.
•Compare different transfer learning models (custom
CNN, VGG, ResNet, DenseNet) to observe the performance of each model in pneumonia detection, not just
assuming one architecture is sufficient.
•A hybrid ensemble of the two best-performing pretrained
models with soft voting probability aggregation, designed
to achieve higher accuracy, stability and recall than any
single model can achieve on its own.
•Web-based deployment of the final ensemble pipeline, so
that it can be used for real-time diagnostic support
particularly in areas with limited medical resources.