A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network (DCNN) framework for brain tumor classification that uses EfficientNet-B6 as the backbone architecture and adds additional layers. The model achieved an accuracy of 99.10% on the public Brain Tumor MRI datasets, and we performed an ablation study to determine the optimal batch size, optimizer, loss function, and learning rate to maximize the accuracy and robustness of the model, followed by K-Fold cross-validation and testing the model on an independent dataset, and tuning Hyperparameters with Bayesian Optimization to further enhance the performance. When comparing our model to other deep learning (DL) models such as VGG19, MobileNetv2, ResNet50, InceptionV3, and DenseNet201, as well as variants of the EfficientNet model (B1–B7), the results show that our proposed model outperforms all other models. Our investigational results demonstrate superiority in terms of precision, recall/sensitivity, accuracy, specificity, and F1-score. Such innovations can potentially enhance clinical decision-making and patient treatment in neurooncological settings.

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection (SERAD). SERAD is used to detect and classify the four most happening road anomalies, such as accidents, car fires, fighting, and snatching, through road surveillance videos with high accuracy. The SERAD adapted three pre-trained Convolutional Neural Networks (CNNs) models, namely VGG19, ResNet50 and InceptionV3. The stacking technique is employed to incorporate these three models, resulting in much-improved accuracy for classifying road abnormalities compared to individual models. Additionally, it presented a custom real-world Road Anomaly Dataset (RAD) comprising a comprehensive collection of road images and videos. The experimental results demonstrate the strength and reliability of the proposed SERAD model, achieving an impressive classification accuracy of 98.7%. The results indicate that the proposed SERAD model outperforms than the individual CNN base models.