Implementation of Vision Transformers on SPECT Heart Dataset: A Comparative Study

Medical imaging has gone through many changes and one of the changes could be observed since the introduction of transformers and deep learning models. Transformers have complicated architecture that can help in carrying out complex tasks on medical images or data. This research paper presents a comprehensive study on the implementation of the Vision Transformers (ViTs) on the Single-Photon Emission Computed Tomography (SPECT) heart dataset. The basic objective is to evaluate the accuracy of the classification of abnormal cardiac conditions from SPECT images using ViT. Further, this paper tries to explore different aspects of the architecture of transformers and their performance in comparison to the traditional machine learning models in analyzing the SPECT heart dataset. It helps to contribute to the growing community of research carried out in medical images and data using new technologies.