Improving Brain Cancer Detection with a CNN-RNN Hybrid Model: A Spatial-Temporal Approach

Abstract

Accurate and early detection of brain cancer is critical for improving treatment outcomes and patient survival. However, traditional diagnostic methods relying on radiological interpretation often lead to variable accuracy and delayed diagnoses due to the complex nature of brain tumors. This paper presents a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Recurrent Neural Networks (RNNs) for temporal analysis, specifically designed to improve brain cancer detection from MRI and CT scans. By leveraging the strengths of both CNNs and RNNs, the model captures intricate spatial and temporal patterns in medical images, leading to significant improvements in detection accuracy, sensitivity, and specificity. Comparative evaluations show that the proposed hybrid model outperforms conventional diagnostic techniques and existing deep learning approaches. The results highlight the potential of this method for earlier and more reliable brain cancer diagnoses, ultimately contributing to more personalized and effective treatment plans. Furthermore, the paper suggests that this hybrid approach could be adapted for the detection of other complex medical conditions.