Brain Tumor Detection Using Hybrid Deep Learning Approaches

This study introduces a novel hybrid deep learning framework for automated brain tumor detection in MRI scans. The methodology combines convolutional neural networks (CNN) with long short-term memory (LSTM) networks to enhance the capture of spatial and sequential features. Additionally, advanced preprocessing techniques, such as wavelet transform and anisotropic diffusion filtering, improve data clarity, while Gabor filters and Discrete Wavelet Transform (DWT) are employed for texture and feature extraction. Leveraging pretrained EfficientNet models with transfer learning ensures high accuracy and efficiency. Deployed on a cloud-based system, this approach supports real-time clinical analysis, demonstrating potential for accurate and early diagnosis.