This project explores the potential of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, implemented through transfer learning, for the classification of brain tumors. By leveraging pre-trained models on extensive datasets, this approach aims to enhance the accuracy and efficiency of brain tumor classification. The integration of CNNs allows for effective feature extraction from complex medical images, such as MRIs, while LSTMs contribute to analyzing sequential data, potentially improving the interpretation of tumor progression over time. This synergy aims to offer a robust tool for medical diagnosis, aiding in the timely and precise identification of various brain tumor types.

Keywords: Brain Tumor Classification, Convolutional Neural Networks, Long Short-Term Memory, Transfer Learning, Medical Imaging, MRI, Deep Learning, Artificial Intelligence.