Brain Tumour Prediction Using Temporal Memory

Abstract

Brain tumor prediction plays a critical role in advancing early diagnosis and effective treatment planning, directly impacting patient survival rates. Traditional methods for detecting brain tumors involve extensive image processing and manual feature extraction, which can be time-consuming and prone to errors. Recent advancements in deep learning have introduced neural networks, specifically Long Short-Term Memory (LSTM) networks, as effective tools for handling the sequential nature of medical imaging data. This study presents an approach leveraging LSTM-based models for brain tumor prediction, focusing on capturing temporal dependencies in MRI scans. By utilizing a time-sequence approach to model variations in patient data, the LSTM model effectively identifies and classifies tumor presence with improved accuracy. Through extensive training on labeled MRI datasets, the proposed method demonstrates high predictive performance, reducing the need for manual feature engineering and setting a new standard in automated brain tumor detection.