Pneumonia Detection From Chest X-Rays Using Deep Learning : A Comprehensive Review

Abstract

Pneumonia is a major global cause of morbidity
and mortality, particularly affecting young children and the
elderly, and early and accurate detection remains essential to
reduce fatalities and optimize resource allocation in clinical
settings [1]. Manual chest X-ray interpretation is commonly
used but suffers from inter-observer variability, diagnostic delays
and lack of availability of expert radiologists in many regions
[2]. Advances in artificial intelligence and deep learning have
enabled automated, reproducible, and rapid analysis of chest
radiographs using convolutional neural networks (CNNs), transfer
learning, vision transformers, and hybrid architectures, often
achieving radiologist-level performance on curated benchmarks
[3], [4]. In this work we present a comprehensive, experimentally
validated pipeline for pneumonia detection that integrates a
custom CNN trained from scratch with multiple transfer-learning
backbones (VGG, ResNet, DenseNet, Inception, EfficientNet),
ensemble strategies, vision transformer variants (ViT, Swin,
hybrid CNN+ViT), and feature-extraction + classical classifier
baselines (CNN→SVM, RF, KNN). The pipeline emphasizes
clinical priorities by optimizing sensitivity/recall, calibrating
predicted probabilities, and quantifying predictive uncertainty
for triage applications. We describe in detail the preprocessing,
augmentation, loss functions (binary cross-entropy and focal loss),
regularization, optimization, and interpretability with Grad-
CAM, and provide extensive comparisons on public chest Xray
benchmarks (RSNA, NIH ChestX-ray8, Kaggle Pneumonia)
plus external holdouts. Results demonstrate that well-regularized
custom CNNs and ensembles achieve high sensitivity with competitive
overall AUC, while hybrid and transformer models offer
gains when sufficient data or transfer pretraining is available [5],
[6].We conclude by describing system deployment considerations,
limitations, and prioritized future directions such as federated
learning, explainable AI (XAI), multi-disease detection, and
lightweight models for edge inference.