Sep 2026· Conference Proceedings in Science and Management· 0 citations· 2 references
TL;DR
A hybrid machine learning model for brain tumor detection is proposed in which a pretrained MobileNetV2 convolutional neural network (CNN) extracts deep features from MRI images and a linear support vector machine (SVM) classifies them into four classes: glioma, meningioma, pituitary tumor, and no tumor.
Abstract
Brain tumors are among the most dangerous and life-threatening neurological diseases, and early detection is essential for successful treatment and improved patient survival. Magnetic resonance imaging (MRI) is the most widely used modality for the rapid diagnosis of brain tumors, but the accurate segmentation and interpretation of MRI images remain challenging. Deep learning (DL) has recently brought significant progress in the identification and categorization of brain tumors. This paper proposes a hybrid machine learning model for brain tumor detection in which a pretrained MobileNetV2 convolutional neural network (CNN) extracts deep features from MRI images and a linear support vector machine (SVM) classifies them into four classes: glioma, meningioma, pituitary tumor, and no tumor. The performance of the model is reported with class-wise precision and recall and a confusion matrix.
An autonomous deep learning framework for brain tumor detection and classification using advanced medical imaging techniques has the potential to improve diagnostic accuracy, reduce cognitive load on medical professionals, and support radiologists in making timely and accurate clinical decisions.
M. Sali· International Journal of Com...· 0 citations
In this work, automatic brain tumor detection will be proposed by using Convolutional Neural Networks (CNN) classification, and the deeper architecture design will be performed by using small kernels, the weight of the neuron will be given as small.
Rajshri Shelke, Tanmay Sutar, Suraj Gayakwad· Journal of Science & Tec...· 0 citations
Brain tumors pose a critical threat to human health, often resulting in significant neurological impairments and high mortality rates. Early and accurate detection is essential for improving patient outcomes; however, conventional diagnostic methods, such as magnetic resonance imaging (MRI) interpretation by radiologis...
Ghaida Alsharef, R. Asiri, Lama Alsultan et al.· Bulletin of Electrical Engin...· 0 citations
This research addresses the critical task of brain tumor detection and classification by leveraging advanced neural networks and deep learning techniques. The study focuses on detecting three prevalent types of brain tumors—glioma, meningioma, and pituitary tumors—as well as identifying cases without tumors. Two cuttin...
Amal Alshahrani· Engineering, Technology &...· 0 citations
The framework offers an efficient and promising basis for automated screening; broader multi-institutional and patient-level validation is still required before it could be expected to reduce dependency on manual interpretation or to support clinical decision-making.
Aditya Pandey, Swapnil Shinde, Sana Nalband et al.· Journal of Intelligent &...· 0 citations
The developed framework provides a reliable and efficient computer-aided diagnosis tool for early brain tumor detection and can assist radiologists in clinical decision-making and personalized treatment planning.
G. Jeyalakshmi· Natural Resources for Human...· 0 citations
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