CerebroNet: A systematically derived explainable brain tumor classifier for resource-constrained MRI diagnostics

While Artificial Intelligence (AI) has demonstrated potential in assisting MRI-based brain tumor diagnosis, its real-world utility appears to be limited by high computational demands and demographic biases inherent in Western-centric datasets. This study proposes a systematic optimization framework to develop a resource-efficient, explainable classifier tailored to an underrepresented South Asian cohort. We initially benchmarked 21 deep learning architectures to isolate the experimentally optimal teacher model. Subsequently, "CerebroNet" was derived via architectural ablation and refined through a holistic pipeline integrating knowledge distillation and unstructured pruning. The resulting model contains merely 0.637 million parameters, representing a 17-fold reduction relative to state-of-the-art benchmarks. Despite this substantial compression, CerebroNet seems to retain over 96% of the teacher's diagnostic fidelity, attaining 95.79% accuracy on augmented data and 96.21% on raw clinical scans. Validation on an external dataset yielded 91% accuracy, suggesting a degree of robust generalization. Furthermore, explainable AI (Layer-CAM) analysis indicated that the student model likely preserves the relevant visual reasoning of the teacher. By balancing computational efficiency with demographic inclusivity, this work attempts to offer a reproducible strategy for implementing reliable AI diagnostics in resource-constrained healthcare environments.