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Brain tumors are aberrant cells. Medical imaging helps diagnose and classify brain tumors. MRI-based brain tumor categorization is a potential medical imaging research area. Patient's tumor sizes and features vary in brain images. Radiologists struggle to classify tumors from many photos. This research proposes an efficient Deep Learning (DL)-based tumor classification system. This research presents three CNN models for brain tumor classification. VGG16, VGG19, and SqueezeNet. Image compression reduces storage space and allows detailed analysis of brain images, which must be stored for long periods for research and medicinal purposes. For medical brain picture storage, this study uses JPEG2000. Classification is done with and without compression to determine how reduction affects classification performance. The classification models reveal that VGG16 has 98.5% accuracy, VGG19 98.80%, and SqueezeNet 98.7% without reduction. The weighted average model outperforms all other base models at a 20 K-fold value of 98.8%.