A Review on Enhancing Brain Tumor Detection Accuracy in MRI Images Using Deep Learning and LSTM Techniques
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Abstract
Brain tumors represent one of the most serious and life-threatening conditions in the field of neuro-oncology. Early and accurate detection plays a crucial role in improving treatment outcomes and survival rates. Magnetic Resonance Imaging (MRI) is widely used for brain tumor diagnosis due to its superior soft-tissue contrast and non-invasive nature. However, manual interpretation of MRI scans is time-consuming, subject to human error, and varies between radiologists. To overcome these challenges, recent research has focused on integrating deep learning methods for automated and accurate tumor detection. This review explores advancements in deep learning, particularly the use of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, in enhancing the accuracy of brain tumor detection in MRI images. CNNs are effective in spatial feature extraction, while LSTMs provide temporal understanding, making hybrid CNN-LSTM models particularly valuable for volumetric or sequential imaging data. The study discusses various model architectures, performance metrics, data preprocessing techniques, and challenges such as overfitting, interpretability, and dataset imbalance. By comparing current methodologies, the review provides insights into how deep learning techniques are revolutionizing brain tumor diagnostics and highlights directions for future research to improve clinical applications.
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