An Optimized Deep Wavelet Autoencoder System for Detecting Tumors in Brain

In recent years, brain imaging techniques have gained substantial prominence in enhancing anatomical understanding and informing medical diagnostic planning, particularly in the domain of brain tumor analysis. Among these, Magnetic Resonance Imaging (MRI) stands out for its ability to provide high-resolution images critical for the identification and evaluation of diverse brain tumor types. This study proposes a comprehensive architecture for automated MRI image processing and brain tumor detection, leveraging the robust classification and segmentation capabilities of Deep Neural Networks (DNN). Central to this approach is the introduction of a novel Deep Wavelet Autoencoder (DWAE), which synergistically combines the multi-resolution analysis strength of wavelet transforms with the dimensionality reduction efficacy of autoencoders, thereby improving feature extraction and classification performance. The integration of preprocessing techniques further enhances diagnostic precision by delineating and isolating relevant brain regions while minimizing noise and irrelevant features. The proposed DNN-DWAE model was empirically validated on a publicly available dataset from Kaggle, comprising 7,000 MRI images categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor. Each image was resized to 256 × 256 pixels to ensure uniformity in input dimensions. The dataset was partitioned into 70% for training and 30% for testing to facilitate robust model evaluation. Experimental results demonstrate that the DNN-DWAE model achieves a classification accuracy of 96%, outperforming several existing methods and underscoring its potential for enhancing automated tumor detection in clinical MRI analysis. The findings suggest that the proposed framework may offer substantial support to radiologists and medical professionals by improving the accuracy, consistency, and efficiency of brain tumor diagnosis.