Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet.

ABSTRACT: Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest in explainable artificial intelligence (XAI), which helps to develop methods of visualizing, explaining, and analyzing deep learning models, has increased recently. With explainable artificial intelligence, it is possible to understand whether the solutions offered by deep learning techniques are safe. This paper aims to diagnose a fatal disease such as a brain tumor faster and more accurately using XAI methods. In this study, we preferred datasets that are widely used in the literature, such as the four-class kaggle brain tumor dataset (Dataset I) and the three-class figshare brain tumor datas

SUBMITTER: Tascı B 

PROVIDER: S-EPMC10000758 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature