# A Comparative Study of CNN, ResNet, and Vision Transformers for Multi-Classification of Chest Diseases Source: [A Comparative Study of CNN, ResNet, and Vision Transformers for Multi-Classification of Chest Diseases]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:50:15.439Z Authors: Ananya Jain, Aviral Bhardwaj, Kaushik Murali, Isha Surani Published: 2024-05-31T23:56:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()