Stroke is a sudden interruption of blood supply to brain. This is due to lack of oxygen caused by blockage of blood flow. Machine learning (ML) considered as a branch of artificial intelligence which is effective in spotting complex patterns in large medical data. ML is well suited in large medical applications especially those that depends on complex protomic and genomic measurement. There are several ML techniques that are used for various disease detection and predictions. This paper mainly focused on such techniques and feature selection mechanism that are useful for detecting stroke.
The two most evident modalities of humans are language and vision. Any system that aids interaction between human beings and Artificial Intelligence (AI) is rooted upon these two. Text-to-Image synthesis (T2I) powered by Natural Language Processing (NLP) and deep Generative Adversarial Networks (GANs) replicates this phenomenon. The logical relationship between semantics and vision guides T2I, that attempts to translate highly detailed natural language textual descriptions to pixel-level details. The human concept of attention is leveraged and conceptualized by deep attentional multi-layered GANs. Mimicking the human thinking processes of visualizing the scenes in mind while speaking and listening can be extensively used in various AI applications that craves brain-like comprehending potency. The advancement of a multitude of GANs that focused on semantic consistency, high-resolution photo-realistic images and diversity in synthesis has been investigated in this article.
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