Brain tumor detection is crucial for early diagnosis and treatment planning in the field of medical image analysis, particularly in neuroimaging. Deep learning techniques, including transfer learning, have shown great promise in this regard. This research investigates the effectiveness of utilizing the MobileNet architecture for brain tumor detection in neuroimaging. MobileNet is a lightweight convolutional neural network designed for efficient deployment on resource-constrained and mobile devices. Its focus on both accuracy and computational efficiency makes it a promising option for medical image analysis. In this methodology, pretrained MobileNet models, previously trained on extensive image datasets, are fine-tuned using a carefully curated neuroimaging dataset. The experiments cover various neuroimaging modalities, such as MRI and CT scans, demonstrating MobileNet’s ability to adapt to brain tumor characteristics despite limited annotated data through transfer learning. A comprehensive evaluation using publicly available benchmark datasets is conducted, and the results are compared to established architectural approaches. This study highlights the potential of transfer learning with MobileNet for brain tumor detection, emphasizing the advantages of using pretrained architectures to address data scarcity in medical imaging. Ultimately, this approach contributes to enhancing the accuracy and efficiency of brain tumor detection pipelines, aiding medical professionals in making timely diagnoses and improving patient care.