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 >

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 >

Biomedical Informatics and Smart Healthcare are helping to enhance and improve modern medicine and healthcare facilities by promoting technology-enhanced care and patient-centric approaches. This editorial primarily discusses the latest advancements in the fields of artificial intelligence, big data analytics, and IoT-enabled medical devices that are redefining medical workflows, diagnostics, and healthcare delivery. As the field evolves, biomedical informatics has enabled personalized patient treatment, real-time health tracking, and data-driven decision-making. The editorial also addresses ethical and regulatory concerns associated with this medical transformation, including issues related... More >

Diabetic Retinopathy occurs when elevated blood sugar levels damage retinal blood vessels, potentially leading to vision impairment. In this paper, we have tested the performance of CNN, ViT and their hybrid models. The dataset used is publicly available on Kaggle and the dataset contained around 35,000 retinal images which were divided into 5 classes namely No DR, Mild DR, Moderate DR, Severe DR and Proliferative DR. In CNN we tested 4 different architectures in which we achieved the best accuracy of 75.4% with Resnet50 architecture and with ViT model we achieved an accuracy of 83.9% and from the hybrid model we achieved an accuracy of 88.4% from the Resnet50 + ViT. The results shown by the... More >