Version 1
: Received: 13 August 2023 / Approved: 14 August 2023 / Online: 14 August 2023 (08:34:12 CEST)
How to cite:
Santos, D. Brain Tumor Detection using the VGG-16 Model: A Deep Learning Approach. Preprints2023, 2023080983. https://doi.org/10.20944/preprints202308.0983.v1
Santos, D. Brain Tumor Detection using the VGG-16 Model: A Deep Learning Approach. Preprints 2023, 2023080983. https://doi.org/10.20944/preprints202308.0983.v1
Santos, D. Brain Tumor Detection using the VGG-16 Model: A Deep Learning Approach. Preprints2023, 2023080983. https://doi.org/10.20944/preprints202308.0983.v1
APA Style
Santos, D. (2023). Brain Tumor Detection using the VGG-16 Model: A Deep Learning Approach. Preprints. https://doi.org/10.20944/preprints202308.0983.v1
Chicago/Turabian Style
Santos, D. 2023 "Brain Tumor Detection using the VGG-16 Model: A Deep Learning Approach" Preprints. https://doi.org/10.20944/preprints202308.0983.v1
Abstract
This article presents a study on brain tumor detection using the VGG-16 model, a convolutional neural network known for its performance in computer vision tasks. The aim of the study is to classify magnetic resonance imaging (MRI) images and accurately identify the presence of brain tumors. The dataset used consists of brain tumor MRI images, categorized into two classes: "NO" (no tumor) and "YES" (tumor). The methodology involves setting up the environment, importing and preprocessing the data, building the VGG-16 model, and evaluating its performance using metrics such as accuracy, precision, and recall. The results demonstrate an accuracy of approximately 88% on the validation set and 80% on the test set, indicating the potential of the VGG-16 model in supporting healthcare professionals in diagnosing brain tumors. The study contributes to the field of medical image analysis and offers insights into the application of deep learning for brain tumor detection.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
