Onah, Daniel; Desai, Ravish; (2025) DeepBrainNet: An Optimized Deep Learning Model for Brain Tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning. In: Proceedings of the 2025 IEEE International Conference on Big Data (IEEE BigData 2025). IEEE: Macau, China. (In press).

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Abstract

Early and accurate brain tumour classification from MRI is challenging due to inter-scanner variability, ambiguous boundaries, and limited labelled data. This paper presents DeepBrainNet, a lightweight yet powerful hybrid model that combines EfficientNetB0 and ResNet50 with transfer learning and fuzzy C-means guided feature selection. Three public MRI sources—figshare, Br35H, and SARTAJ—were harmonized through de-duplication and label audits, with patient-wise stratified splits to ensure reliability. A brief hyperparameter search identified Adam optimizer with learning rate 1e-4, batch size 32, cosine learning rate decay, label smoothing, and early stopping as the best settings. On the combined dataset, DeepBrainNet achieved an accuracy of 88.9 percent, a weighted F1-score of 89%, and a macro-AUC of 98%. The model demonstrates strong performance and scalability while maintaining computational efficiency. The study also outlines a Big Data pathway through mixed-precision and distributed training, reporting throughput, latency, and memory utilization as key system metrics.

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