Riesz-Quincunx WaVnet Chaotic Rime Attention Encoder and ensemble convolutional neural networks for remote brain tumor diagnosis

BackgroundAccurate classification and segmentation of brain tumors from medical images are essential for reliable diagnosis and treatment planning. However, variations in image quality, noise, and tumor characteristics can complicate automated analysis.ObjectiveThis study aims to develop a deep learning framework for the accurate classification and segmentation of brain tumors by integrating an ensemble convolutional neural network (CNN) with the Riesz-Quincunx WaVnet Chaotic Rime Attention Encoder.MethodsThe proposed framework incorporates image preprocessing techniques to enhance contrast, reduce noise, and improve image quality. The ensemble CNN is integrated with the Riesz-Quincunx WaVnet Chaotic Rime Attention Encoder to strengthen feature extraction and representation. The framework is designed to identify brain abnormalities and classify tumor subregions, including necrotic tumor cores, enhancing tumors, peritumoral edematous areas, and non-enhancing tumor cores. Furthermore, a Flask-based web application is developed to facilitate remote access and support clinical collaboration.ResultsThe proposed framework is designed to improve brain tumor classification and segmentation by combining ensemble learning with enhanced feature representation. However, quantitative performance metrics are required to substantiate its diagnostic accuracy, segmentation performance, and superiority over existing methods.ConclusionThe proposed deep learning framework integrates image preprocessing, ensemble CNN classification, and attention-based feature extraction to support automated brain tumor analysis. Its Flask-based deployment offers potential for remote accessibility and telemedicine applications, supporting medical specialists in diagnostic decision-making. Further validation using quantitative performance metrics and independent clinical datasets is necessary to establish its clinical reliability.