A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model

doi:10.1590/1678-4324-2024231137

Paper

A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model

Published Jan 1, 2024 · Hafiz Muhammad Tayyab Khushi, Tehreem Masood, A. Jaffar

Brazilian Archives of Biology and Technology

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Abstract

: World's deadliest disease is brain tumor. Misdiagnosed cancers and inadequate treatment reduce survival. However, magnetic resonance imaging (MRI) is utilized for tumor analysis, but the enormous number of pictures produced by MRI makes it time-consuming and difficult to diagnose a patient just because of its complex nature, putting their life at risk. Thus, accurately detecting early brain cancers manually is difficult. We need an autonomous, intelligent system to detect brain cancers early and accurately. This study proposes a pre-trained EfficientNetb4 model with an adjusttable learning rate and custom callback to efficiently classify tumors. The proposed methodology improves the quality and quantity of the publicly accessible Br35h dataset

Study Snapshot

The EfficientNetb4 model effectively classifies brain tumors using MRI data, improving the quality and quantity of the publicly accessible Br35h dataset.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model

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