Advancements In Artificial Intelligence for Brain Tumor Detection: A Comprehensive Survey

Authors

  • Rameesha Zia Quaid-e-Azam University,Islamabad

Keywords:

Brain tumor, Artificial Intelligence, Tumor detection, Diagnosis, Image Preprocessing, Deep Learning, Neural Networks, Molecular biomarkers.

Abstract

Brain tumors are a group of diseases characterized by abnormal growths of cells in the brain that can cause severe neurological symptoms. In recent years, the advent of artificial intelligence (AI) techniques has shown great promise in enhancing brain tumor detection. This survey research discusses methods and techniques used for AI-based brain tumor detection. Brain tumors pose significant health risks, necessitating accurate and timely detection for effective treatment. The study defines brain tumors and emphasizes the need for precise detection methods due to tumor variations. Biomarkers associated with brain tumors are investigated, highlighting their potential as diagnostic and prognostic indicators. The utilization of deep learning (DL) models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and 3D CNNs, is examined, providing a comparative analysis of their strengths and limitations. The importance of datasets, such as TCIA, BRATS, and ISLES, is discussed in training and evaluating AI models for brain tumor detection. This survey aims to contribute to the understanding and progress of AI-based brain tumor detection along with the comparison of some deep learning models, providing insights for researchers and healthcare professionals working towards improving patient outcomes.

 

Keywords: Brain tumor; Artificial Intelligence; Tumor detection; Diagnosis; Image Preprocessing; Deep Learning; Neural Networks, Molecular biomarkers.  

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Published

2023-12-29

How to Cite

Zia, R. (2023). Advancements In Artificial Intelligence for Brain Tumor Detection: A Comprehensive Survey . University of Wah Journal of Computer Science, 5, 1–20. Retrieved from http://uwjcs.org.pk/index.php/ojs/article/view/66