Efficientnet-Based And YOLO-Driven Brain Tumor Detection And Segmentation

DOI

10.2991/978-94-6463-858-5_49How to use a DOI?

Keywords

Brain tumor detection; EfficientNet; deep learning; YOLO; Transfer Learning; segmentation; medical imaging; classification

Abstract

The medical diagnosis of brain tumors is challenging due to the intricate and complex nature of these tumors. The development of this research antecedents precision tumor classification and segmentation through the integration of transfer learning, EfficientNet, and YOLO. We proposed the EfficientNet-B0 model which classifies the tumor as pituitary, meningioma, glioma, or no tumor. In YOLOv8, segmentation is done on an object on the frame level that helps in identifying the precise localization of tumor site. Transfer learning allows pre-trained weights to be utilized which reduces the amount of training needed as well as improves performance on sparse medical imaging datasets. Using transfer learning enhances the generalization of the model. The accuracy, efficiency in computation, and application potential of the framework are profoundly magnified as shown by experimental results. In addition, this approach solved the problem of timely and accurate detection of tumors significantly improving the patients health and decreasing medical practitioners workload.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Najla Musthafa
AU  - Mohammed Aflah
AU  - Minhaj Akavalappil
AU  - A. Mohammed Jasim
AU  - Mohammed Aseel
AU  - Shanid Malayil
AU  - A. K. Mubeena
PY  - 2025
DA  - 2025/11/04
TI  - Efficientnet-Based And YOLO-Driven Brain Tumor Detection And Segmentation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 565
EP  - 575
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_49
DO  - 10.2991/978-94-6463-858-5_49
ID  - Musthafa2025
ER  -