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Around the world, brain tumors are becoming the leading cause of mortality. The inability to undertake a timely tumor diagnosis is the primary cause of this pandemic. Brain cancer diagnosis is a crucial procedure that relies on the expertise and experience of the doctor. Radiologists must use an automated tumor classification model to find brain cancers. The current model’s accuracy has to be improved to get suitable therapies. Radiologists can consult various computer-aided diagnostic (CAD) models in the literature on medical imaging to assist them with their patients. Previous research has widely used CNN models for tumor detection and classification, which typically require large datasets. This research proposed the Caps-VGGNet hybrid model, which integrates the CapsNet model with the VGGNet model by adding the layers of VGGNet. The presented model addresses the challenge of requiring large …