Hybrid Deep Learning Model for Robust Brain Tumor Detection: Integrating Filtering and Feature Extraction

Brain tumor diagnosis remains a major obstacle in medical imaging since effective treatment planning requires precise identification and classification. Advanced filtering approaches are needed to improve the diagnostic accuracy of imaging modalities such as MRI (magnetic resonance imaging) and computed tomography (CT). Although deep learning has greatly increased the diagnostic accuracy of brain tumor recognition, problems with MRI image preprocessing still exist related to disturbances, relics, and low contrast. By including sophisticated filtering methods as the wavelet Transformation, which is Bilateral Filtration, and Guided Filtering, which improves on conventional deep learning-based tumor categorization. To extract reliable tumor features, an integrated deep learning model that combines Efficient Net, the DenseNet and BiLSTM is proposed. With an accuracy of 97.5% as opposed to 90% in conventional CNN models, the results show enhanced segmentation efficiency, classification precision, and superior Grad-CAM visuals. The results show that the combination of sophisticated filters greatly enhances image quality, enabling more accurate tumor segmentation and classification. With the potential to improve diagnostic results offers insights into how these techniques might be incorporated into clinical procedures. Metrics like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are used to examine the filters. Despite retaining the structural details and image quality that are crucial to precise tumor diagnosis. These results show how important preprocessing and enhancement are to improve the diagnosis of brain tumors.