The first MRI scan was done in the year 1978 by researchers at EML Laboratories. As per an estimate, approximately 251,329 people died due to primary cancerous brain and CNS (Central Nervous System) Tumors in the year 2020. It has been recommended by various medical professionals that brain tumor detection at an early stage would help in saving many lives. Whenever radiologists deal with a brain MRI they try to diagnose it with the histological subtype which is quite subjective and here comes the major issue. Upon that, in developing countries like India, where there is 1 doctor for every 1151 people, the need for efficient diagnosis to help radiologists and doctors come into picture. In our approach, we aim to solve the problem using swin transformers and deep learning to detect, classify, locate and provide the size of the tumor in the particular MRI scan which would assist the doctors and radiologists in increasing their efficiency. At the end, the medics would be able to download the predictions and measures in a PDF (Portable Document Format). Keywords: brain tumor, transformers, classification, medical, deep learning, detection

翻译：首次MRI扫描于1978年由EML实验室的研究人员完成。据估计，2020年约有251,329人死于原发性恶性脑肿瘤和中枢神经系统肿瘤。多位医学专家建议，早期检测脑肿瘤有助于挽救更多生命。放射科医生在处理脑部MRI时，通常需要根据组织学亚型进行诊断，这具有一定主观性，而这正是主要问题所在。此外，在印度等发展中国家，每1151人中仅有1名医生，因此需要高效诊断来辅助放射科医生和临床医生。我们的方法旨在利用Swin Transformer和深度学习技术，检测、分类、定位特定MRI扫描中的肿瘤并提供其尺寸，从而帮助医生和放射科医生提高诊断效率。最终，医疗人员能够以PDF（便携式文档格式）下载预测结果和测量数据。关键词：脑肿瘤；Transformer；分类；医学；深度学习；检测