Saliency Object Detection-Based Medical Image Fusion concentrates on the most prominent anatomical parts that naturally attract attention. The process begins with a two-step pipeline. First, it identifies the salient regions those structures most critical to a diagnostic decision. These highlighted areas are then fed into a guided-filtering framework. This work blends them with complementary information from a second imaging modality, such as CT and MRI. The method uses total variation regularization to suppress noise while preserving edges. A saliency-based weighting scheme ensures that every key detail is retained. The result is a single, high-quality image that carries the full diagnostic... More >

Early diagnosis plays a critical role in the successful skin cancer treatment. To classify skin lesions as benign or malignant, this study suggested a deep learning-enabled method that combines a Transformer module with ResNet50. At first ResNet50, a powerful convolutional neural network, to extract the image features and then enhance the model with a Transformer layer to improve the model accuracy is used. The final model is fine-tuned to achieve better accuracy. This approach shows improved classification results compared to the traditional model. The results signify that combining CNN-based feature extraction with Transformer-enabled global attention suggestively improves skin lesion clas... More >

This study investigates the role of feature extraction and dimensionality reduction techniques in addressing high-dimensional image data, with a particular focus on Alzheimer's disease classification using 2D magnetic resonance imaging (MRI). Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are employed to extract discriminative features from MRI images; however, due to the high dimensionality of the extracted features, dimensionality reduction is required. Principal Component Analysis (PCA) is utilized to reduce feature dimensionality while preserving most of the relevant information, as reflected in the improved performance of the underlying machine learning (ML) class... More >

The rapid evolution of the Internet of Medical Things (IoMT) has enabled pervasive patient monitoring through Wireless Body Area Networks (WBANs). However, energy depletion, high path-loss, link instability, and latency remain major barriers to achieving reliability in real-time healthcare applications. Existing schemes, such as Distance Aware Relaying Energy-efficient (DARE) and Link Aware and Energy Efficient Scheme for Body Area Networks (LAEEBA), mitigate individual constraints, distance and link quality respectively, but lack holistic optimization across energy, distance, and reliability dimensions. This paper proposes HEART (Hybrid Energy-Aware Routing Technique), a dual-sink, clusteri... More >

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Accurate diagnosis from histopathological biopsy samples is essential, yet manual examination is time-consuming and subject to inter-observer variability, particularly given the shortage of trained pathologists alongside the increasing number of cases. Deep learning, especially Convolutional Neural Networks (CNNs), has emerged as a powerful tool for classifying medical images by automatically extracting discriminative features from raw data. In this study, we investigate the use of the publicly available Breast Cancer Histopathological (BreakHis) image database, which contains benign and malignant... More >

Fusion of multi-modal medical images has transformed healthcare by overcoming the limitations of single-modality imaging, where modalities such as CT, MRI, PET, and SPECT provide complementary information. This review systematically traces the evolution of multi-modal medical image fusion from conventional mathematical models to state-of-the-art artificial intelligence (AI) techniques. We examine the transition from classical approaches---such as multiscale transformations, wavelet decompositions, and sparse representation---to modern deep learning methods, including convolutional neural networks, generative adversarial networks, and transformer architectures. Key limitations of existing met... More >

Long non-coding RNAs (lncRNAs) are important for plant growth, how plants respond to stress, and their overall development. However, it can be difficult to identify them accurately because they come in many structures and can look similar to coding RNAs. In this study, we introduce DeepPInc V2.0, a new tool that uses deep learning to analyze both the sequence and the secondary structure of RNAs, combining them in a DenseNet-CNN hybrid model. DeepPInc V2.0 outperforms existing tools on various plant datasets, achieving an accuracy of 94.2%, an F1-score of 0.93, and a Matthews Correlation Coefficient (MCC) of 0.88. It consistently outperforms seven leading tools in this area. Importantly, the... More >

Exorbitant expenses, lengthy development periods, and a high incidence of drug candidate attrition plague the conventional pharmaceutical R&D pipeline---a problem sometimes referred to as ``Eroom's Law.'' By radically reorganizing the discovery process, generative artificial intelligence (AI), which has emerged as a transformational force, promises to buck this tendency. Through data synthesis on key performance metrics, this review offers a thorough analysis of the effects of AI-enhanced methodologies. We explore how a new set of tools is changing the paradigm from experimental screening to in silico design. These tools include graph neural networks (GNNs)—a class of neural architectures... More >