Using deep learning models to detect brain tumors
Molloy Faculty Mentor
Helen Dang
Presenter Major
Computer Science
Presentation Type
Oral
Location
Hays Theater, Wilbur Arts Building, Molloy University
Start Date
1-5-2026 11:27 AM
End Date
1-5-2026 11:33 AM
Description (Abstract)
The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Therefore, this study investigates novel and highly efficient deep learning-based classification models to distinguish brain images with meningioma tumors from brain images without meningioma tumors. In this research, we will use the publicly available Meningioma Tumor dataset. We will use several different deep learning models to conduct experiments on this dataset. Experimental comparative evaluations will be provided for each model. This will assist in clinical diagnosis and early detection of meningioma tumors.
Related Pillar(s)
Study
Using deep learning models to detect brain tumors
Hays Theater, Wilbur Arts Building, Molloy University
The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Therefore, this study investigates novel and highly efficient deep learning-based classification models to distinguish brain images with meningioma tumors from brain images without meningioma tumors. In this research, we will use the publicly available Meningioma Tumor dataset. We will use several different deep learning models to conduct experiments on this dataset. Experimental comparative evaluations will be provided for each model. This will assist in clinical diagnosis and early detection of meningioma tumors.
