CNN-Based Approach for Brain Tumor Detection and Severity Prediction

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Author(s): J. Jenefa (Christ University, India), R. Athulya (Deloitte Parks Vista, India), Aaron John Varghese (Sabre Travel Technologies, India), Divya Vetriveeran (Christ University, India)and Rakoth Kandan Sambandam (Christ University, India)
Copyright: 2025
Pages: 20
Source title: Future Innovations in the Convergence of AI and Internet of Things in Medicine
Source Author(s)/Editor(s): Velliangiri Sarveshwaran (National Chung Cheng University, Taiwan), Karthikeyan Periyaswami (National Chung Cheng University, Taiwan)and Keping Yu (University of Hosei, Japan)
DOI: 10.4018/979-8-3693-7703-1.ch006

Keywords: Artificial Intelligence / Computer Science & IT / Medical Diagnosis and Practice / Medical Information Science Reference

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Abstract

Artificial intelligence is widely used in healthcare, especially in medical imaging. It leads to advanced diagnosis using innovative approaches to analyze complex data more accurately and provide personalized treatments. This helps the clinicians efficiently analyze the imaging data, leading to early detection of diseases like brain tumors, cancer, cardiovascular diseases, etc. The research work focuses on the detection and severity prediction of brain tumors. Magnetic resonance imaging (MRI) scan images are preprocessed in the proposed model using different methods. The convolutional neural network model (CNN) is used to detect and predict brain tumors and can be used in personalized treatments. The proposed method has an accuracy of about 98% in classification and severity prediction.

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CNN-Based Approach for Brain Tumor Detection and Severity Prediction

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