Deep Learning and Ensemble Models for Brain Tumor Classification using Medical Imaging

Abstract

The proposed research focuses on the early and accurate detection of brain tumors using advanced deep learning and ensemble models based on medical imaging data to enhance diagnostic outcomes. The dataset consists of 4234 MRI images collected from publicly available sources and Kaggle repositories, comprising 1592 no-tumor, 649 glioma, 999 meningioma, and 994 pituitary tumor images. Data augmentation and cleaning techniques were applied to increase diversity and reduce class imbalance. The dataset was divided into 80% for training and 20% for testing, and models such as Convolutional Neural Networks (CNN), MobileNetV2, and Multi-Layer Perceptron (MLP) were employed. To improve robustness, 5-fold cross- validation was conducted, achieving a mean testing accuracy of 0.97 ± 0.01, with precision and recall values exceeding 0.95. The ensemble model adopts a meta-model stacking architecture, where predictions from CNN and MobileNetV2 are combined as inputs to an MLP classifier, achieving significantly higher results than baseline models such as VGG16 (0.90) and ResNet50 (0.92). The system was implemented as a Streamlit-based web application, demonstrating real-time clinical usability and scalability in hospital environments. The proposed model’s high accuracy, stability, and lightweight design make it an efficient, fast, and reliable solution to support clinicians in brain tumor diagnosis and improve patient care.