Enhanced Brain Tumor Classification from MRI Images Using Deep Learning Model

In the realm of image classification, traditional algorithms, encompassing both machine learning and deep learning, grapple with formidable challenges arising from uneven pixel ranges and dimensionality reduction. This results in a significant impediment to achieving accurate image categorization. Numerous examples of such traditional methods, including KNN, Random Forest, SVM, DNN, CNN etc, have encountered persistent issues such as inefficient performance of feature engineering, limited accuracy, etc. In response to these challenges, this paper introduces a novel image classification method that integrates pixel mapping, DWT, and CNN for improved efficiency and reliability. By resolving irregular pixel ranges through initial pixel mapping, our method establishes uniformity as a foundation for subsequent image analysis. Subsequently, DWT is employed to dissect and reduce image dimensionality, extracting essential features while lowering computational complexity. This two-step preprocessing approach forms a robust foundation for effective data classification. Within this framework, our proposed CNN architecture plays a pivotal role, utilizing both spectral and spatial information to address image categorization challenges. The network’s capacity to learn complex patterns enhances classification accuracy. In extensive evaluations, our methodology surpasses conventional classification techniques, yielding impressive results. With an Overall Accuracy (OA) of 96.9% and a Kappa statistic of 95.16%, our method showcases excellence and practical potential. These compelling achievements underscore the significance of our approach in tackling image classification challenges, paving the way for enhanced precision and efficiency across various domains.