Diagnosis of Human Brain Tumor Based on Complex Image Processing Techniques using CNN

DOI

10.2991/978-94-6463-858-5_263How to use a DOI?

Keywords

Brain Tumor Detection; MRI; Image Segmentation; Supervised Learning; Feature extraction; Convolutional Neural Networks

Abstract

Brain tumor detection is increasingly becoming an issue in the medical field. A brain tumor is a growth or mass of abnormal cells in your brain cells develop uncontrollably. MRI (Magnetic Resonance Imaging) plays one of the primary roles in brain tumor detection where the tumor segmentation is required to identify the abnormal region from the tumor. This work is utilized in conjunction with machine learning methods to aid in tumor detection. Utilizing sophisticated image processing methods, the technique provides segmentation and classification of brain tumors within the MRI scans. Secondarily, the supervised learning models of CNN can be a diagnostic tool to distinguish between a normal and an abnormal tissue and inform medical personnel whether the patient has a problem and initiate a treatment process for the patient. It indicates how several Machine learning techniques are better than straightforward techniques in precise identification. In healthcare terminology, it is a useful tool since it saves time and reduces errors. Experimental results confirmed the proposed model’s capability to accurately predict the existence of brain tumors. Based on that knowledge, guided, convolutional neural networks can generate about 91% accuracy and 92.7%efficiency.

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  - K. Venkateswara Rao
AU  - Pitla Vaishnavi
AU  - Shaik Latheef Baba
AU  - B. Kiran Teja Reddy
PY  - 2025
DA  - 2025/11/04
TI  - Diagnosis of Human Brain Tumor Based on Complex Image Processing Techniques using CNN
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3156
EP  - 3164
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_263
DO  - 10.2991/978-94-6463-858-5_263
ID  - Rao2025
ER  -