O Uso de Aprendizado Profundo na classificação de ressonâncias magnéticas para detecção de tumor cerebral

Abstract

The proposed work aims to explore the application of computer vision techniques in the classification of MRI scans to facilitate the diagnosis of brain tumors. Early diagnosis of brain tumors is essential for appropriate treatment and better outcomes for patients. With the advancement of technology and the increasing use of medical images, the use of machine learning algorithms has shown promise in this field. The work is based on the use of two widely known architectures: Transformers and Convolutional (CNN). These architectures are capable of extracting relevant features from images, which is essential for detecting brain tumors using computer vision. The literature presents the models that obtained the best results from each architecture, along with image pre-processing techniques, which will be used to highlight relevant characteristics and reduce image noise. The aim is that this work contributes to advances in the early diagnosis of brain tumors through the application of computer vision techniques and shows the importance of using artificial intelligence in the health sector. The use of architectures such as Transformers and Convolutional, making a comparison between computer vision architectures.