Deep Learning based Brain Tumour Segmentation

Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various image...

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Veröffentlicht in:WSEAS TRANSACTIONS ON COMPUTERS 2021-01, Vol.19, p.234-241
Hauptverfasser: Pattabiraman, V., Singh, Harshit
Format: Artikel
Sprache:eng
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Zusammenfassung:Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN based Semantic Segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architecture and experiment with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss function like dice loss function, hierarchical dice loss function cross entropy, etc.
ISSN:1109-2750
2224-2872
DOI:10.37394/23205.2020.19.29