Computer Vision

Pneumonia Detection with Vision Transformers

Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data

95.48% accuracy · 96.18% F1

Teaching a Vision Transformer to spot pneumonia on chest X-rays by looking at the whole image at once, not just local patches.

The problem

Convolutional models see an X-ray through small windows and can miss patterns that only make sense across the whole lung field.

The approach

A diagnostic pipeline built around a Vision Transformer, whose self-attention captures long-range dependencies and context across the radiograph.

The outcome

Outperformed traditional approaches on a comprehensive chest X-ray dataset, with 95.48% accuracy and 96.18% F1.

In detail

Built a Vision Transformer pipeline for pneumonia detection from chest X-ray images. The model uses patch-based self-attention to capture global context and subtle spatial relationships that can be difficult for conventional convolutional architectures to represent. In the reported experiments, the ViT approach achieved 95.48% test accuracy and a 96.18% F1 score, outperforming the evaluated baselines.

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By Kanishka Ranaweera, PhD, Machine Learning Engineer in Melbourne, Victoria, Australia. hello@kanishkabuilt.me