Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging.

Project Code :TCMAPY632

Objective

In the proposed system, data augmentation ad transfer learning techniques were used for the detection of brain tumor in MRI images

Abstract

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 tumours. By leveraging pre-trained models on extensive datasets, this approach aims to enhance the accuracy and efficiency of brain tumour 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 tumour progression over time. This synergy aims to offer a robust tool for medical diagnosis, aiding in the timely and precise identification of various brain tumour types.

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

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.