Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques.

COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.

Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.

Alzheimer's disease is a progressive neurodegenerative disorder that has been shown to increase in frequency in individuals with mild cognitive impairment. This study proposes a deep learning (DL)approach for the categorical diagnosis of Alzheimer's using MRI images. It employs Federated Learning (FL) for privacy preservation and Local Interpretable Modelagnostic Explanations (LIME) for explainability. The model uses a convolutional neural network (CNN) for classification, incorporating data decentralization to ensure patient confidentiality. FL allows multiple clients to train the model without exchanging any raw data, thus providing a security mechanism for sensitive kinds of medical information. Specifically, a federated setup is used where three clients have different datasets, and Krum aggregation is applied to enhance robustness. In addition, Adaptive Synthetic Sampling (ADASYN) is used to address class imbalance. The model was assessed within the framework of both a centralized setup and a federated approach, achieving 98.33% classification accuracy in the centralized training and 91.72% in the FL scheme. Thus, FL protects data privacy while still attaining high classification performance. While federated training slightly lowers the accuracy, it increases data security and hence mitigates data-sharing risks in a centralized manner. We use LIME to provide the explainability needed by the clinician to retrain their decision, thus raising trust in diagnostics. The research proves to be a big endorsement for employing FL in medical imaging applications for the effective yet responsible detection of Alzheimer's, a step toward aiding diagnosis and treatment strategies.

Convolutional neural networks (CNNs) have demonstrated impressive performance in image recognition tests due to their deep hierarchical feature extraction capabilities. But when it comes to satellite imagery, which frequently has inherent uncertainties like shifting lighting, unclear object boundaries, and overlapping classes, these conventional models provide serious difficulties. Standard CNN architectures may perform less well as a result of these ambiguities. To address these limitations, this study introduces a novel hybrid approach called Fuzzy VGG, which integrates fuzzy logic into the VGG16 and VGG19 architectures. The primary objective is to enhance model interpretability and robustness in handling the uncertainties present in satellite image data. Our method replaces the conventional dense-softmax output layer with a custom fuzzy classification layer that represents soft class membership using cosine similarity. Our experiments on the UC Merced Land Use dataset show that the integration of fuzzy logic leads to significantly improved performance. The Fuzzy VGG16 model achieved an accuracy of 90%, while Fuzzy VGG19 reached 97% accuracy. This represents a consistent performance gain of 15% over the standard VGG16 (75% accuracy) and VGG19 (80% accuracy) models. These results confirm the effectiveness of our hybrid system in creating a more robust and reliable decision-making mechanism for complex image classification tasks. On a medical dataset, the fuzzy VGG model achieved an overall accuracy of 96%, with macro and weighted averages for precision, recall, and F1-score all equal to 96%.