2026· CITA-DW· pp. 165-178· 0 citations· 22 references
Computer Science
TL;DR
A comparative study of two deep learning approaches for the binary classification of grayscale brain MRI images, applied to a public dataset available on Kaggle, demonstrating the clinical potential of both approaches for diagnostic support, while highlighting the need for validation prior to any clinical deployment.
This research systematically benchmarks five CNN architectures (VGG19, DenseNet201, ResNet50, Inception-v3, and MobileNet) on balanced and naturally imbalanced MRI datasets, suggesting that VGG19 is particularly good at discriminative performance.
Tegar Anugrah Firdaus, B. Rais, Marcelinus Jonathan Salim et al.· 0 citations
Brain tumors represent one of the most critical and life-threatening forms of cancer worldwide, and accurate automated classification of MRI scans plays a crucial role in supporting timely diagnosis and treatment planning. In this study, a deep learning-based approach for automatic brain tumor classification is propose...
Kalhara Batangala, A. Amarasinghe, U. Wijenayake· Moratuwa Engineering Researc...· 0 citations
Brain tumors pose a critical threat to human health, often resulting in significant neurological impairments and high mortality rates. Early and accurate detection is essential for improving patient outcomes; however, conventional diagnostic methods, such as magnetic resonance imaging (MRI) interpretation by radiologis...
Ghaida Alsharef, R. Asiri, Lama Alsultan et al.· Bulletin of Electrical Engin...· 0 citations
The proposed explainable deep learning framework shows great promise of helping clinical diagnosis of brain tumors to be reliable and transparent, by integrating with AI.
Mohd. Yousuf, Joy Chowdhury, Susmoy Chowdhury et al.· American Journal of Applied...· 0 citations
Brain tumors constitute a significant global health challenge, and accurate, timely diagnosis is critical for effective treatment planning. Manual interpretation of magnetic resonance imaging (MRI) remains time-consuming, error-prone and subject to inter-observer variability. The objective of this study was to develop...
Adukwu Nasiru Ukwuteno, G. Obunadike, B. T. Mashi· Journal of Basics and Applie...· 0 citations
The findings demonstrate the potential of combining complementary transfer-learning models with preprocessing, ensemble fusion, and explainable artificial intelligence for automated brain tumor classification from MRI images.
Ahmed Thijeel· Alkadhim Journal for Compute...· 0 citations
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