Welcome to the InfoSci Platform
Using SVM and CNN as Image Classifiers for Brain Tumor Dataset

Using SVM and CNN as Image Classifiers for Brain Tumor Dataset

Maryam Zia, Hiba Gohar
Copyright: © 2023 |Pages: 24
ISBN13: 9781668486962|ISBN10: 1668486962|ISBN13 Softcover: 9781668486979|EISBN13: 9781668486986
DOI: 10.4018/978-1-6684-8696-2.ch008
Cite Chapter Cite Chapter

MLA

Zia, Maryam, and Hiba Gohar. "Using SVM and CNN as Image Classifiers for Brain Tumor Dataset." Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science, edited by Soly Mathew Biju, et al., IGI Global, 2023, pp. 202-225. https://doi.org/10.4018/978-1-6684-8696-2.ch008

APA

Zia, M. & Gohar, H. (2023). Using SVM and CNN as Image Classifiers for Brain Tumor Dataset. In S. Biju, A. Mishra, & M. Kumar (Eds.), Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science (pp. 202-225). IGI Global. https://doi.org/10.4018/978-1-6684-8696-2.ch008

Chicago

Zia, Maryam, and Hiba Gohar. "Using SVM and CNN as Image Classifiers for Brain Tumor Dataset." In Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science, edited by Soly Mathew Biju, Ashutosh Mishra, and Manoj Kumar, 202-225. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8696-2.ch008

Export Reference

Mendeley
Favorite

Abstract

Brain tumors make up 85% to 90% of all primary central nervous system (CNS) malignancies. Over a thousand people are diagnosed with cancer each year, and brain tumors are one of those fatal illnesses. It is challenging to diagnose this because of the intricate anatomy of the brain. Medical image processing is expanding rapidly today as it aids in the diagnosis and treatment of illnesses. Initially, a limited dataset was utilized to develop a support vector machine (SVM) model for the classification of brain tumors. The tumors were classified as either present or absent. As the dataset was small, the SVM model achieved great accuracy. To increase the dataset's size, data augmentation, an image pre-processing technique was used. Due to the SVM's limitations in producing high accuracy over a large dataset, convolutional neural network (CNN) was used to produce a more accurate model. Using both SVM and CNN aided in drawing comparisons between deep learning techniques and conventional machine learning techniques. MRI scans were used for tumor classification using the mentioned models.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.