Research Article
Leveraging Classification of Brain Tumour using Deep Learning Architectures
@INPROCEEDINGS{10.4108/eai.7-12-2021.2314487, author={Karunya K and Karpagam G.R and Swathi J}, title={Leveraging Classification of Brain Tumour using Deep Learning Architectures}, proceedings={Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India}, publisher={EAI}, proceedings_a={ICCAP}, year={2021}, month={12}, keywords={brain tumour deep learning classification comparison cnn resnet50}, doi={10.4108/eai.7-12-2021.2314487} }- Karunya K
Karpagam G.R
Swathi J
Year: 2021
Leveraging Classification of Brain Tumour using Deep Learning Architectures
ICCAP
EAI
DOI: 10.4108/eai.7-12-2021.2314487
Abstract
Despite advances in human intellect and biomedical in the last few decades, people continue to suffer from various cancers due to their volatile nature. This disease is still a major issue for the entire humanity. Brain tumour is one of the most crucial and serious illnesses. Oftheentire primary central nervous system tumours, Brain tumorsmake up 85 to 90%. It isestimatedthat 18,600 adults, including 8,100 women and 10,500 men, will die of primary cancerous tumors of the brain and centralnervoussystem tumors this year. Among the children of various age groups also it is seen as one of the most crucial cancers. Thus, accurate and timely handling of this disease is decisive. In order to speed up the process of brain tumour detection (augmented with accuracy, reliability and experience)Deep learning models can be used.To efficiently diagnose brain tumour kinds and compare classification performance, the proposed work makes optimal use of a newly modelled Convolutional Neural Network and ResNet 50,a pre-trained network.