Brain tumor is one of the most aggressive diseases nowadays, resulting in a very short life span if it is diagnosed at an advanced stage. The treatment planning phase is thus essential for enhancing the quality of life for patients. The use of Magnetic Resonance Imaging (MRI) in the diagnosis of brain tumors is extremely widespread, but the manual interpretation of large amounts of images requires considerable effort and is prone to human errors. Hence, an automated method is necessary to identify the most common brain tumors. Convolutional Neural Network (CNN) architectures are successful in image classification due to their high layer count, which enables them to conceive the features effectively on their own. The tuning of CNN hyperparameters is critical in every dataset since it has a significant impact on the efficiency of the training model. Given the high dimensionality and complexity of the data, manual hyperparameter tuning would take an inordinate amount of time, with the possibility of failing to identify the optimal hyperparameters. In this paper, we proposed a Bayesian Optimization-based efficient hyperparameter optimization technique for CNN. This method was evaluated by classifying 3064 T-1-weighted CE-MRI images into three types of brain tumors (Glioma, Meningioma, and Pituitary). Based on Transfer Learning, the performance of five well-recognized deep pre-trained models is compared with that of the optimized CNN. After using Bayesian Optimization, our CNN was able to attain 98.70% validation accuracy at best without data augmentation or cropping lesion techniques, while VGG16, VGG19, ResNet50, InceptionV3, and DenseNet201 achieved 97.08%, 96.43%, 89.29%, 92.86%, and 94.81% validation accuracy, respectively. Moreover, the proposed model outperforms state-of-the-art methods on the CE-MRI dataset, demonstrating the feasibility of automating hyperparameter optimization.

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims. EEG signal decoding is a difficult process due to its complexity and poor signal-to-noise ratio. Convolutional neural networks (CNN) have demonstrated their ability to extract time–space characteristics from EEG signals for better classification results. However, to discover dynamic correlations in these signals, CNN models must be improved. Hyperparameter choice strongly affects the robustness of CNNs. It is still challenging since the manual tuning performed by domain experts lacks the high performance needed for real-life applications. To overcome these limitations, we presented a fusion of three optimum CNN models using the Average Ensemble strategy, a method that is utilized for the first time for MI movement classification. Moreover, we adopted the Bayesian Optimization (BO) algorithm to reach the optimal hyperparameters’ values. The experimental results demonstrate that without data augmentation, our approach reached 92% accuracy, whereas Linear Discriminate Analysis, Support Vector Machine, Random Forest, Multi-Layer Perceptron, and Gaussian Naive Bayes achieved 68%, 70%, 58%, 64%, and 40% accuracy, respectively. Further, we surpassed state-of-the-art strategies on the BCI competition IV-2a multiclass MI database by a wide margin, proving the benefit of combining the output of CNN models with automated hyperparameter tuning.

This study introduces a hybrid framework for classifying bone fractures from X-ray images, aiming to enhance diagnostic accuracy and interpretability. Utilizing the Bone Fracture Multi-Region X-ray Dataset, we fine-tune the ConvNeXt V2 model for binary classification. Features extracted from the CNN are optimized using RFECV and employed to train an ensemble of classifiers, with a Random Forest meta-learner integrating their predictions. Our method achieves a test accuracy of 99.21%, with precision, recall, and F1-score of 98.53%, 100%, and 99.26%, respectively. Interpretability is addressed through Grad-CAM and saliency maps, providing insights into the model's decision-making process. This approach marks a significant step forward in automated fracture diagnosis, combining high performance with transparency essential for clinical trust and adoption.