E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||5|
|Published online||07 October 2021|
A Flexible Accession for Brain Tumour Detection and Classification using AI Methodologies: Survey
1 MTech Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
* Corresponding author: email@example.com
In order to get a successful and appropriate treatment for the disorder regarding health, précised and identifying it early is much important in the scenario of brain tumor treatment. Prior knowledge and detection of the tumor helps to cope up with good medication, and also helps in saving a life in due time. Bio-medical informatics(BI) and Computer aided diagnosis(CAD) are benefiting neurooncologists in many ways. Machine learning algorithms are now used to do Image processing on medical images and contrast with the information due to manual diagnosis of Brain tumor which is always a tedious task because of human error is indulged. When compared with manual traditional practices, Computer aided mechanisms are compared to obtain better results. In this paper we are presenting the existing models or architectures overview of various researchers who dedicatedly addressed and worked on this tedious task.
Key words: —Random forest (RF) / Expectation-maximization(EM) / Decision Tree(DT) / CNN / DNN
© The Authors, published by EDP Sciences, 2021
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