Deep Learning-Based MRI Brain Tumor Classification: Evaluating Sequential Architectures for Diagnostic Accuracy

Accurate classification of brain tumors from MRI scans plays a vital role in assisting clinical diagnosis and treatment planning. This project investigates and compares three deep learning-based classification approaches designed to evaluate the effectiveness of integrating recurrent layers into conventional convolutional architectures. Specifically, a CNN-LSTM model, a CNN-RNN model with GRU units, and a baseline CNN classifier using EfficientNetB0 are developed and assessed on a curated MRI dataset.

The CNN-LSTM model uses ResNet50 as a feature extractor, with spatial features reshaped and passed through stacked LSTM layers to explore sequential learning on static medical images. The CNN-RNN model implements TimeDistributed convolutional layers followed by GRUs, examining the potential benefits of GRU-based modeling. The EfficientNetB0-based CNN model, trained end-to-end without recurrent components, serves as the performance baseline.

All three models are evaluated using training accuracy, validation loss, confusion matrices, and class-wise performance metrics. Results show that the CNN-LSTM architecture provides the most balanced performance across tumor types, while the CNN-RNN model suffers from mild overfitting. The EfficientNetB0 baseline offers stable and efficient classification for general benchmarking.