Work

Brain Tumor Analysis by Extracting Features and Classification Through Support Vector Machines

Year: 2024

Type: article

Abstract: Brain tumours are an increasing global epidemic, claiming millions of lives each year. Misdiagnosis can result in needless therapy and reduced life expectancy. Doctors have used computer-based diagnostic techniques such as DenseNet201 and the Gabor Filter to produce accurate diagnoses. In this work, SVM was used to classify independent features, and essential features were collected from an MRI image dataset using the DenseNet201 algorithm and Gabor filter. Deep convolutional layers outperform standard techniques in terms of extracting unique characteristics from target areas. An MRI dataset of 7023 brain tumour pictures from the Kaggle website was utilised to classify features using SVM. The hybrid approach of DenseNet201 and Gabor Filter produced the best overall results, with 98.02% precision, 98.01% accuracy, and 98.01% F1 score.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13268792

Authors: SABA KHAN, KHALID REHMAN

Institution: CECOS University

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6911630435

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