# Deep Learning for Automatic Pneumonia Detection

arXiv: https://arxiv.org/abs/2005.13899 · HTML: https://mlsanity.com/paper/2005.13899/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 20 of 20)

- [AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray](https://mlsanity.com/paper/2105.09937/) (2021)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [UMIT: Unifying Medical Imaging Tasks via Vision-Language Models](https://mlsanity.com/paper/2503.15892/) (2025)
- [3D Convolutional Neural Networks for Stalled Brain Capillary Detection](https://mlsanity.com/paper/2104.01687/) (2021)
- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays](https://mlsanity.com/paper/2104.03218/) (2021)
- [Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays](https://mlsanity.com/paper/2207.11393/) (2022)
- [Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution](https://mlsanity.com/paper/2306.14933/) (2023)
- [Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion](https://mlsanity.com/paper/2504.03135/) (2025)
- [Generative Residual Attention Network for Disease Detection](https://mlsanity.com/paper/2110.12984/) (2021)
- [Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis](https://mlsanity.com/paper/2111.08606/) (2021)
- [Decoupling Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/2210.08336/) (2022)
- [Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time](https://mlsanity.com/paper/2212.08058/) (2022)
- [Prediction of Pneumonia and COVID-19 Using Deep Neural Networks](https://mlsanity.com/paper/2308.10368/) (2023)
- [Lung Diseases Image Segmentation using Faster R-CNNs](https://mlsanity.com/paper/2309.06386/) (2023)
- [FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations](https://mlsanity.com/paper/2312.01871/) (2023)
- [A Deep Look Into -- Automated Lung X-Ray Abnormality Detection System](https://mlsanity.com/paper/2404.04635/) (2024)
- [Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification](https://mlsanity.com/paper/2503.18973/) (2025)
- [SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection](https://mlsanity.com/paper/2509.01080/) (2025)
- [SP-Det: Self-Prompted Dual-Text Fusion for Generalized Multi-Label Lesion Detection](https://mlsanity.com/paper/2512.04875/) (2025)

## Abstract (arXiv)

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.