Complementary Analysis of Deep Learning Architectures for Brain Tumor Classification Using MRI Images
DOI:
https://doi.org/10.17720/2k4c9183Keywords:
Convolutional Neural Network (CNN), ResNet50, InceptionV3, Brain tumor, and transfer learningAbstract
Accurate brain tumor classification from MRI scans is critical for effective diagnosis and treatment planning. This study compares the performance of three deep learning models—Simple CNN, ResNet50, and InceptionV3—in classifying brain tumors using MRI images. Among the models, InceptionV3 outperformed others, achieving the highest accuracy and generalization to unseen data, making it the most suitable for real-world applications. ResNet50 displayed competitive performance but showed overfitting tendencies, while the Simple CNN served as a baseline model with limited complexity and accuracy. Future improvements, including the incorporation of transfer learning, attention mechanisms, and larger datasets, are suggested to enhance model robustness and clinical reliability. These findings demonstrate the potential of advanced deep learning models in automating brain tumor classification for clinical use.
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