EFFICIENTNET-BASED MULTI-CLASS BRAIN TUMOR CLASSIFICATION FROM MRI IMAGES: A ROBUST DEEP LEARNING FRAMEWORK

Abstract

Magnetic resonance imaging is widely used for the examination of brain abnormalities because it provides detailed soft-tissue information without ionizing radiation. Nevertheless, manual interpretation of large numbers of MRI slices is time-consuming and may be affected by inter-observer variation. This paper presents a structured deep learning framework for multi-class brain tumor classification using the EfficientNet family of convolutional neural networks. The framework emphasizes consistent preprocessing, transfer learning, class-balanced augmentation, careful validation, and clinically meaningful performance reporting. Rather than treating classification as an isolated model-training task, the proposed approach connects data quality, model calibration, error analysis, and reproducibility. The paper also explains why EfficientNet is suitable for medical image classification: its compound scaling strategy balances network depth, width, and input resolution, enabling strong feature learning with comparatively efficient computation. A complete experimental protocol is described for separating training, validation, and test data at the patient level; controlling information leakage; selecting evaluation metrics; and comparing EfficientNet variants with conventional convolutional baselines. The resulting framework can support reliable thesis-level experimentation and can later be extended with visual explanation methods such as Grad-CAM. The study concludes that model efficiency alone is insufficient; dependable brain tumor classification requires disciplined data handling, transparent reporting, and external validation.