MR.K.L.SUNDEEP,B. PRIYANKA,B. RATNAKAR,K. KEERTHANA,B. RAHUL 03 03 Brain tumors are among the most critical and lifethreatening 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 VGG16 VGG19 ResNet50 InceptionV3 and DenseNet201 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 timeconsuming and often affected by human subjectivity which makes automated detection systems highly valuable The proposed approaches utilize pretrained deep learning models that are finetuned 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 F1score are used to assess and compare the models Experimental results show that ResNet50 provides the best performance due to its deeper architecture and strong feature extraction capability followed by VGG19 and InceptionV3 DenseNet201 demonstrates a good balance between computational efficiency and accuracy while VGG16 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 Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026