Multi-layer Perceptron for Brain Tumor Picture Classification Based on GLCM Feature Extraction

DOI

10.2991/978-94-6463-760-1_24How to use a DOI?

Keywords

Computer Network; Fibre Optic; Inter-building; OSPF Routing

Abstract

The advancement of information technology has propelled developments in image processing, including the identification of textures or patterns, which are primary choices for detecting diseases such as brain tumors. However, epidemiological information in Indonesia remains severely limited. There-fore, this research employs a Multi-Layer Perceptron (MLP) model to classify brain tumors based on MRI images by applying Gray Level Co-Occurrence Matrix (GLCM) feature extraction. MLP is utilized as an artificial neural network algorithm capable of addressing nonlinear classification problems, while GLCM characterizes grayscale patterns within images and provides comprehensive information for classification. The optimal combi-nation of the MLP model obtained includes a learning rate of 0.01, 2 hidden layers, and 200 epochs. The glioma class achieves an accuracy of 80.09% with an AUC value of 0.85, the meningioma class achieves an accuracy of 63.92% with an AUC of 0.64, the pituitary class achieves an accuracy of 83.60% with an AUC of 0.88, and the non-tumor class achieves an accuracy of 83.45% with an AUC of 0.85.

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  - M. Al Haris
AU  - Syifa Aulia
AU  - Rochdi Wasono
PY  - 2025
DA  - 2025/07/03
TI  - Multi-layer Perceptron for Brain Tumor Picture Classification Based on GLCM Feature Extraction
BT  - Proceedings of the 3rd Lawang Sewu International Symposium on Medical and Health Sciences (LEWIS-MHS 2024)
PB  - Atlantis Press
SP  - 268
EP  - 283
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-760-1_24
DO  - 10.2991/978-94-6463-760-1_24
ID  - AlHaris2025
ER  -