Alzheimer’s Disease (AD) is a significant cause of dementia worldwide, and its progression from mild to severe affects an individual’s ability to perform daily activities independently. The accurate and early diagnosis of AD is crucial for effective clinical intervention. However, interpreting AD from medical images can be challenging, even for experienced radiologists. Therefore, there is a need for an automatic diagnosis of AD, and researchers have investigated the potential of utilizing Artificial Intelligence (AI) techniques, particularly deep learning models, to address this challenge. This study proposes a framework that combines a Vision Transformer (ViT) and a Gated Recurrent Unit (GRU) to detect AD characteristics from Magnetic Resonance Imaging (MRI) images accurately and reliably. The ViT identifies crucial features from the input image, and the GRU establishes clear correlations between these features. The proposed model overcomes the class imbalance issue in the MRI image dataset and achieves superior accuracy and performance compared to existing methods. The model was trained on the Alzheimer’s MRI Preprocessed Dataset obtained from Kaggle, achieving notable accuracies of 99.53% for 4-class and 99.69% for binary classification. It also demonstrated a high accuracy of 99.26% for 3-class on the AD Neuroimaging Initiative (ADNI) Baseline Database. These results were validated through a thorough 10-fold cross-validation process. Furthermore, Explainable AI (XAI) techniques were incorporated to make the model interpretable and explainable. This allows clinicians to understand the model’s decision-making process and gain insights into the underlying factors driving the AD diagnosis.

As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL) based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavours that similarly showcased enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we made use of Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model’s predictions, and to identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas.

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.