# A Comparative Study of CNN, ResNet, and Vision Transformers for Multi-Classification of Chest Diseases

arXiv: https://arxiv.org/abs/2406.00237 · HTML: https://mlsanity.com/paper/2406.00237/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)

## Built on by (most cited first, 3 of 3)

- [FIN: Fast Inference Network for Map Segmentation](https://mlsanity.com/paper/2510.00651/) (2025)
- [MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classification](https://mlsanity.com/paper/2510.22070/) (2025)
- [Distributed Deep Learning for Medical Image Denoising with Data Obfuscation](https://mlsanity.com/paper/2511.06006/) (2025)

## Abstract (arXiv)

Large language models, notably utilizing Transformer architectures, have emerged as powerful tools due to their scalability and ability to process large amounts of data. Dosovitskiy et al. expanded this architecture to introduce Vision Transformers (ViT), extending its applicability to image processing tasks. Motivated by this advancement, we fine-tuned two variants of ViT models, one pre-trained on ImageNet and another trained from scratch, using the NIH Chest X-ray dataset containing over 100,000 frontal-view X-ray images. Our study evaluates the performance of these models in the multi-label classification of 14 distinct diseases, while using Convolutional Neural Networks (CNNs) and ResNet architectures as baseline models for comparison. Through rigorous assessment based on accuracy metrics, we identify that the pre-trained ViT model surpasses CNNs and ResNet in this multilabel classification task, highlighting its potential for accurate diagnosis of various lung conditions from chest X-ray images.