ItemArticle

Brain Tumor Classification Using an Ensemble of Deep Learning Techniques

Title
Brain Tumor Classification Using an Ensemble of Deep Learning Techniques
Creator
Patro S.G.K.; Govil N.; Saxena S.; Kishore Mishra B.; Taha Zamani A.; Ben Miled A.; Parveen N.; Elshafie H.; Hamdan M.
Description
The article reflects on the classification of brain tumors where several deep learning (DL) approaches are used. Both primary and secondary brain tumors reduce the patient's quality of life, and therefore, any sign of the tumor should be treated immediately for adequate response and survival rates. DL, especially in the diagnosis of brain tumors using MRI and CT scans, has applied its abilities to identify excellent patterns. The proposed ensemble framework begins with the image preprocessing of the brain MRI to enhance the quality of images. These images are then utilized to train seven DL models and all of these models recognize the features related to the tumor. There are four models which are General, Glioma, Meningioma, and Pituitary tumors or No Tumor model, which helps in reaching a joint profitable prediction and concentrating solely on the strength of the estimation and outcome. This is a significant improvement over all the individual models, attaining a 99. 43% accuracy. The data used in this research was gotten from Kaggle website and comprised of 7023 images belonging to four classes. Future work will focus on increasing the dataset size, investigating additional DL architectures, and enhancing real-Time detection to improve the accuracy of diagnostic scans and their overall relevance to clinical practice. 2013 IEEE.
Source
IEEE Access, Vol-12, pp. 162094-162106.
Date
2024-01-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Brain tumor; deep learning; ensemble; glioma; meningioma; MRI; pituitary
Coverage
Patro S.G.K., Woxsen University, School of Technology, Hyderabad, 502345, India; Govil N., GLA University, Department of Computer Engineering and Applications (CEA), Mathura, 281406, India; Saxena S., CHRIST University, Department of Computer Science, Bengaluru, 560074, India; Kishore Mishra B., NIST University, Department of Computer Science and Engineering, Berhampur, 761008, India; Taha Zamani A., Northern Border University, Department of Computer Science, Arar, 91431, Saudi Arabia; Ben Miled A., Northern Border University, Department of Computer Science, Arar, 91431, Saudi Arabia; Parveen N., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Guntur, 522302, India; Elshafie H., King Khalid University, College of Computer Science, Department of Computer Engineering, Abha, 61421, Saudi Arabia; Hamdan M., South East Technological University, Walton Institute for Information and Communication Systems Science, Waterford, X91 HE36, Ireland
Rights
Restricted Access
Relation
ISSN: 21693536
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Patro S.G.K.; Govil N.; Saxena S.; Kishore Mishra B.; Taha Zamani A.; Ben Miled A.; Parveen N.; Elshafie H.; Hamdan M., “Brain Tumor Classification Using an Ensemble of Deep Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13454.

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