Leveraging u-net architecture for accurate localization in brain tumor segmentation

Poo, Jeckey Ng Kah and Saealal, Muhammad Salihin and Mohd Zamri, Ibrahim and Marlina, Yakno (2023) Leveraging u-net architecture for accurate localization in brain tumor segmentation. In: Proceeding - IEEE 9th Information Technology International Seminar, ITIS 2023. 9th IEEE Information Technology International Seminar, ITIS 2023 , 18 - 20 October 2023 , Batu Malang. pp. 1-6. (197293). ISBN 979-835030683-5 (Published)

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Abstract

This study presents an approach based on deep learning to segment brain tumors in medical imaging accurately. The segmentation of brain tumors plays a crucial role in diagnosing, planning treatments, and monitoring disease progression. However, existing methods have limitations such as time-consuming procedures, inadequate accuracy, and delayed detection. The U-Net model architecture, a widely used convolutional neural network (CNN) for medical image segmentation tasks, was employed to segment brain tumors in CT and MRI scans to overcome these challenges. The performance of the U-Net model was evaluated on datasets consisting of 32, 64, and 128 slices, respectively. The results demonstrated the achievement of the highest percentage of mean Intersection Over Union (IOU), with an impressive 80.89% for brain tumor segmentation. These results outperformed other existing methods. The proposed model exhibits the potential to reduce manual segmentation time and subjectivity while enhancing the accuracy of brain tumor diagnosis, treatment planning, and disease monitoring. This research contributes to the field by addressing the problem of brain tumor detection and showcasing the promising results attained using deep learning techniques.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Brain tumor segmentation; Convolutional neural network; Deep learning; MRI scan; U-Net
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	College of Engineering Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	30 Aug 2024 00:15
Last Modified:	30 Aug 2024 00:15
URI:	http://umpir.ump.edu.my/id/eprint/41910
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