Comparative Study of CNN Architectures for Brain Tumor Classification Using MRI: Exploring GradCAM for Visualizing CNN Focus
Year: 2025
Type: conference-paper
Abstract: Brain cancer, with its varied nature, demands early detection for timely treatment. This study aims to refine the diagnosis of brain tumors using convolutional neural network algorithms. Currently, diagnostic accuracy is limited, therefore, our approach uses five different CNN architectures to accurately identify and classify affected brain regions, specifically glioma, meningioma, or pituitary tumors. The AlexNet architecture remarkably achieved training accuracy (99.84%) and validation accuracy (95.19%). By employing GradCAM, heat maps visually clarify the results. This research aims to improve the diagnosis of brain tumors using advanced CNN algorithms. In particular, the success of AlexNet indicates greater diagnostic and treatment potential, promising better outcomes for patients.
Authors: Areli Chinga, Wilden Bendezu, Antonio Angulo
Institution: Universidad San Ignacio de Loyola
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4407178479