A proposed model for automation of detection and classification of brain tumor by deep learning

Ramtekkar, Praveen Kumar; Pandey, Anjana; Pawar, Mahesh

doi:10.1109/idea49133.2020.9170742

Cited by 10 publications

(1 citation statement)

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“…The proposed method had testing success rate of 98.029% and a training success rate of 98.29%. In [14], in order to assist healthcare systems, this study proposed model to automatically detect and classify brain tumor in MRI images that focuses on high accuracy in image segmentation, low noise sensitivity, more processing speed based on DL technology and CNN classifier. The suggested algorithm was based on CNN architecture for brain tumor detection and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Detection Using Deep Learning Technique

Nayak

2024

IJRASET

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One of the most aggressive disorders affecting both children and adults is brain tumors. Brain tumors develop very quickly, and if not treated at the proper time, they decrease the patient's chances of survival. It is crucial to find brain tumors at an early stage. To increase patients’ life expectancy, proper treatment planning and precise diagnostics are most important. Magnetic Resonance Imaging (MRI) is the most effective method for finding brain tumors. Therefore, to find the types of tumors, an automated brain tumor detection system is needed [1]. This work uses Deep Learning (DL) architectures like Convolutional Neural Network (CNN) and EfficientNetB0 for Transfer Learning to detect the brain tumor. This model is used to predict the types of brain tumors

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Section: Literature Reviewmentioning

confidence: 99%