Improved EATFormer: A Vision Transformer for Medical Image Classification

arXiv:2403.13167v1 Announce Type: new
Abstract: The accurate analysis of medical images is vital for diagnosing and predicting medical conditions. Traditional approaches relying on radiologists and clinicians suffer from inconsistencies and missed diagnoses. Computer-aided diagnosis systems can assist in achieving early, accurate, and efficient diagnoses. This paper presents an improved Evolutionary Algorithm-based Transformer architecture for medical image classification using Vision Transformers. The proposed EATFormer architecture combines the strengths of Convolutional Neural Networks and Vision Transformers, leveraging their ability to identify patterns …

abstract algorithm analysis arxiv classification clinicians computer cs.cv diagnosis image images medical paper systems transformer type vision vital