Article
BRAIN-TUMOR DISEASE DETECTION
Brain tumors are among the most critical and life-threatening medical conditions, making early and accurate detection essential for effective treatment planning. The main objective of this project is to study the application of transfer learning using advanced deep learning architectures such as VGG-16, VGG-19, ResNet-50, Inception-V3, and DenseNet-201 for accurate and efficient brain tumor detection from MRI images. These models help address challenges like limited medical imaging data and high computational requirements. Traditional manual analysis of brain MRI scans is time-consuming and often affected by human subjectivity, which makes automated detection systems highly valuable. The proposed approaches utilize pre-trained deep learning models that are fine-tuned to classify brain tumor images effectively. Each model is evaluated using a benchmark dataset, with preprocessing techniques such as normalization, augmentation, and segmentation applied to improve feature extraction. Performance metrics including accuracy, precision, recall, and F1-score are used to assess and compare the models. Experimental results show that ResNet-50 provides the best performance due to its deeper architecture and strong feature extraction capability, followed by VGG-19 and Inception-V3. DenseNet-201 demonstrates a good balance between computational efficiency and accuracy, while VGG-16 performs reliably despite its relatively simple structure. This research highlights the effectiveness of transfer learning in overcoming challenges related to limited data and computational constraints in medical imaging. The findings provide valuable insights for deploying deep learning models in clinical environments, helping improve diagnostic accuracy and efficiency in brain tumor detection.
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