<![CDATA[Brain tumors are conditions characterized by abnormal cell growth in the brain, which can disrupt brain function. Early detection and accurate classification are crucial to ensuring effective treatment. This study aims to improve the accuracy of brain tumor classification by implementing Convolutional Neural Networks (CNN) using Transfer Learning approaches on DenseNet121 and ResNet50 models. Transfer Learning leverages knowledge from pre-trained models on larger datasets, thereby accelerating the training process and enhancing performance on the brain tumor dataset. The dataset used consists of medical images, including images of brain tumors and images without tumors. The data was divided into two parts, with 80% for training and 20% for validation. This split ensures that the model learns optimally from the training data and is tested on unseen data to objectively evaluate its performance. Experimental results show that the ResNet50 model achieved an accuracy of 98.44% on the validation data, while the DenseNet121 model achieved an accuracy of 96.31%. In conclusion, the ResNet50 model outperformed DenseNet121 in brain tumor classification. The implications of this study demonstrate that the Transfer Learning approach with ResNet50 can serve as an effective tool for automated brain tumor diagnosis, potentially improving patient outcomes through more accurate detection and classification]]>
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