Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)). Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*). Effective brain tumor classification is critical for efficient diagnosis and treatment planning in medical applications. This paper employs a Stacked Convolutional Neural Network (SCNN) model for brain tumor classification achieving cutting-edge performance. The suggested technique has been investigated using Br35H::Brain Tumor Detection 2020 dataset. The proposed model outperforms established architectures including MobileNetV2, DenseNet12, ResNet50, and VGG16 across all performance metrics. SCNN can have deeper feature extraction and improved learning capacity making them more powerful than simpler CNN architectures for complex image analysis. 99.47% accuracy, 99.65% precision, 99.30% recall, and a 99.48% F1-score are attained with the SCNN, which demonstrate superior capability in accurately classifying brain tumors with minimal false positives and false negatives. The outcomes emphasize the capability of the proposed model to significantly enhance clinical diagnostic processes. Show AI generated summary Abstract This chapter dives into the transformative potential of stacked convolutional neural networks (SCNN) for brain tumor classification, a critical task in medical diagnostics where precision and speed can significantly impact patient outcomes. The text begins by outlining the challenges of traditional diagnostic methods, including manual feature extraction and the limitations of conventional machine learning approaches, before introducing deep learning as a game-changing solution. At the heart of the discussion is a detailed exploration of SCNN architectures, particularly the VGG16, ResNet50, and MobileNetV2 models, which are compared against the proposed SCNN framework. The chapter meticulously describes the experimental setup, including the Br35H::Brain Tumor Detection 2020 dataset, image