ItemConference Paper

Advancing Brain Tumor Detection with Deep Learning and Machine Learning: A Performance Analysis of Different Deep Learning Models

Title
Advancing Brain Tumor Detection with Deep Learning and Machine Learning: A Performance Analysis of Different Deep Learning Models
Creator
Joshi, Priyanka; Singh, Jagendra; Upreti, Kamal
Description
The current study examines the difficulty of employing a deep learning architecture to diagnose brain tumors quickly and effectively. Our study is built upon a dataset of 253 MRI pictures that have been carefully categorized by medical experts as either positive (Yes) or negative (No) for brain tumors. To guarantee the robustness of model performance, the dataset is carefully divided into training and validation subsets, with 70% set aside for training and 30% for validation. We analyze the diagnostic performance of several machine learning models, including K-Nearest Neighbors (KNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Artificial Neural Networks (ANNs). When these algorithms are applied to MRI scans, brain tumors can be quickly detected, and the increased accuracy makes patient treatment easier. The findings of this study could lead to a rapid and accurate diagnosis of brain tumors, which would greatly enhance patient care and treatment. The results also show how deep learning frameworks can transform medical image processing and diagnosis. This work offers a thorough review of recent findings and techniques for MRI scan-based deep learning-based brain tumor detection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Source
Lecture Notes in Networks and Systems;Volume;1359 LNNS;pp.279-289
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Brain tumor; Deep learning; Heath care; Machine learning; Magnetic Resonance Imaging (MRI); Performance analysis
Coverage
Joshi P., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Upreti K., School of Computer Science, CHRIST University, New Delhi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981964932-7;
Format
online
Language
English
Type
Conference paper

Citation

Joshi, Priyanka; Singh, Jagendra; Upreti, Kamal, “Advancing Brain Tumor Detection with Deep Learning and Machine Learning: A Performance Analysis of Different Deep Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25556.

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