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Ramarkishna V, S.V. Sudheer Kumar, Kuna Dhilli, M. Munisankar
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- Assistant Professor, Assistant Professor, Assistant Professor, Assistant Professor, Sri Padmavati Mahila Visvavidyalayam, Sri Padmavati Mahila Visvavidyalayam, Sri Padmavati Mahila Visvavidyalayam, Sri Padmavati Mahila Visvavidyalayam, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, Andhra Pradesh, India, India, India, India
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Abstract
nBrain tumor detection plays a crucial role in early diagnosis and effective treatment planning. This research presents a MATLAB-based Graphical User Interface (GUI) for Brain Tumor Detection, incorporating a comprehensive pipeline of image processing techniques. The GUI provides a user-friendly platform, empowering medical professionals to accurately and efficiently analyze MRI brain scans. The GUI begins with text removal to eliminate any textual artifacts that may be present in the MRI scans, ensuring a clean input for subsequent processing steps. Image enhancement techniques are then applied to improve the visibility of tumor regions, enhancing the contrast and highlighting subtle features that aid in tumor identification. To address the challenge of noise and artifacts, state-of-the-art noise removal filters are integrated into the GUI. These filters effectively suppress noise while preserving essential structural details, enabling better tumor localization. The presented GUI streamlines the brain tumor detection process, making it accessible to medical practitioners without extensive image processing expertise. By combining text removal, image enhancement, noise removal, thresholding, and segmentation functionalities, the GUI provides a comprehensive and efficient tool for accurate brain tumor diagnosis, ultimately contributing to improved patient outcomes and better healthcare decision-making.
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Keywords: MRI, Brain Tumor, Morphological, GUI, Diagnosis
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References
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- Deepika Sherawat., Sonia., Akshita Rawat. (2021). Brain Tumor Detection Using Machine Learning in GUI. Pp-9-17. Doi: 10.1007/978-981-33-6307-6_2.
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| Volume | 01 |
| Issue | 01 |
| Received | September 8, 2023 |
| Accepted | September 18, 2023 |
| Published | November 23, 2023 |
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