Identifying brain tumors presents a significant challenge in neurological imaging, necessitating precise and effective procedures to expedite diagnosis and treatment planning. In this work, we harness the strengths of Convolutional Neural Networks (CNNs) and the Fuzzy Transform (F-transform) to increase the precision of brain tumor identification. Our primary aim is to create a composite model that integrates advanced feature extraction abilities of CNNs with the F-transform’s capacity for enhancing interpretability and reducing noise. Medical imaging datasets are preprocessed to normalize and segment tumor locations before being trained to extract spatial characteristics using a CNN. These features are then modified with the F-transform to highlight important patterns while eliminating ambiguity. The suggested approach distinguishes tumor-affected regions with an approximate accuracy of 98.8%, indicating high sensitivity and specificity. This hybrid technique exceeds established approaches in precision and resilience, making it a more viable for detecting brain tumors and more informed therapeutic strategies.