Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. This will cause blindness if not identified early. Because DR not an irreversible procedure, and only vision is preserved via care. Consequently, Early diagnosis and care with DR will significantly minimize the chance of vision loss. In modern ophthalmology, retinal image analysis has become a popular approach to disease diagnosis. The ophthalmologists and computerized systems extensively employ fundus angiography to detect DR-based clinical signs for early detection of DR. fundus photographs are commonly prone to low contrast, noise, and irregular illumination issues due to the complexity of imaging environments such as imaging variety of angles and light conditions. This research presents an Algorithm for improving the quality of images to strengthen the standard of color fundus images by reducing the noise and improving the contrast. The approach includes two main stages: cropping the images to remove insignificant content, then applying the shape crop and gaussian blurring for noise reduction and contrast improvement. The experimental results are evaluated using two standard datasets EyePACS and MESSIDOR. It’s clearly shown that the outcomes of feature extraction and classification of enhanced images is outperform the results without applying the enhancement approach. The improved algorithm is also tested in smart hospitals as an IoMT application.

Dark web is a canopy concept that denotes any kind of illicit activities carried out by anonymous persons or organizations, thereby making it difficult to trace. The illicit content on the dark web is constantly updated and changed. The collection and classification of such illegal activities are challenging tasks, as they are difficult and time-consuming. This problem has in recent times emerged as an issue that requires quick attention from both the industry and academia. To this end, efforts have been made in this article a crawler that is capable of collecting dark web pages, cleaning them, and saving them in a document database, is proposed. The crawler carries out an automatic classification of the gathered web pages into five classes. The classifiers used in classifying the pages include Linear Support Vector Classifier (SVC), Naïve Bayes (NB), and Document Frequency (TF-IDF). The experimental results revealed that an accuracy rate of 92% and 81% were achieved by SVC and NB, respectively.

— Diabetic Retinopathy (DR) is a serious consequence of diabetes that seriously impact on the eyes and is a leading cause of blindness. If the lesions in DR arise in the central portion of the fundus, they may result in significant vision loss, which we refer to as Diabetic Macular Edema (DME). Deep learning (DL) techniques are commonly used utilized in ophthalmology for discriminative tasks such as diabetic retinopathy or age-related macular degeneration (AMD) diagnosis. Deep learning techniques typically need huge picture data sets for deep convolutional neural networks (DCNNs) training, it should be graded by human specialists. According to international protocol, it is classified into five severity categories. However, improving a grading model for high generality needs a significant quantity of balanced training data, which is challenging to obtain, especially at high levels of severity. Typical techniques for data augmentation, in many applications of deep learning in the retinal image processing domain, the difficulty of access to huge annotated datasets and legal concerns about patient privacy are limiting issues. As a result, the concept of creating synthetic retinal pictures that are indistinguishable from actual data has garnered more attention. GANs have been certain to be an effective framework for creating synthetic databases of anatomically accurate retinal fundus pictures. GANs, in particular, have garnered increasing attention in ophthalmology. in this article, we present a loss-less generative adversarial network (DR-LL GAN) to generate good resolution fundus pictures that May be adjusted to include random grading and information about the lesion. As a result, large-scale generated data may be used to train a DR grading and lesion segmentation model with more appropriate augmentation. Our model experiments evaluated on IDRID and MESSIDOR datasets, it's obtained a discrimination loss of 0.69374 and a generation loss of 1.10438, as well as a segmentation accuracy of 0.9840 in our tests. This might support in the optimization techniques of the neural network design and in computer-aided screening of medical picture, thus increasing diagnostic reliability for clinical assessment in the future of sophisticated technological healthcare.

The application of artificial intelligence (AI) has transformed medical diagnostics for the better with the adoption and evolution of radiology, histopathology, clinical laboratory testing, robotic surgery, and expert systems. The implementation of deep learning algorithms using AI, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs), have outperformed humans in the analysis of medical images, tumor categorization, and biomarker detection. AI powered biopsy systems, which are a type of robotic assisted diagnostics, have improved the accuracy and reduced the invasiveness of the procedure, which in return, enables for better detection of diseases at earlier stages. The use of pipetting robots and automated blood analyzers for AI powered automation of clinical lab diagnostics improves the speed and volume of diagnostic assays while reducing the rate of human error. Additionally, AI powered expert systems, like OncoKB for the analysis of genetic mutations and deep mind’s AlphaFold for predicting protein structures, are advancing precision oncology and personalized medicine by offering non-biased treatment suggestions. The issues that impede the further integration of AI in diagnostics include data diversity, model generalization, obtaining legal certification, and the absence of XAI which would heighten clinician acceptance. To overcome these barriers, there needs to be standard validation frameworks, federated learning for privacy-centric AI training, and multimodal AI that incorporates medical images, genomic data, and EHRs. Emerging directions in self-supervised learning (SSL), active AI robotics, and human-machine interaction will likely improve the precision of diagnoses, automate clinical processes, and expand healthcare access around the world. This review outlines the role of AI in transforming medical diagnostics while analyzing existing obstacles, emerging possibilities, and recommendations for AI in clinical decision support systems.

Topological Deep Learning (TDL) boosts the capabilities of neural networks by integrating topological features from complex data structures like graphs and simplicial complexes. However, there is a critical gap in the area of structural perturbations within a model and how these affect its stability and generalization. Overfitting and brittle predictions are concerns when dealing with topological noise. We propose a comprehensive theoretical and empirical evaluation framework of TDL robustness. We define two complementary metrics, Topological Drift which quantifies model sensitivity to structural noise using bottleneck distance in persistent homology, and Spectral Variance which measures shifts in the eigenvalues of the Hodge Laplacian. These define the degradation of learned topological representations. We conducted controlled experiments on synthetic Vietoris-Rips complexes, together with real-world data from the TDA Benchmark’s Enzyme Function Prediction dataset. These experiments support our conclusions. High levels of perturbation resulted in a drop in classification accuracy of 19.1%, and an increase in spectral variance of 4.3×, confirming theoretical expectations. Comparison with neglecting frameworks after 2022 on Simplicial Neural Networks and spectral graph models showcases the need for our rigorous dual-metric interpretable approach to stability analysis. Our persistent homology and spectral topology merger lay metric derived from persistent homology to quantify topological drift adaptable for creating TDL architectures with robustness. The results obtained in this study can be used to formulate model equipped with topological state descriptors derived from persistence diagrams and eigen-spectra methods of deep learning for advanced fields like bioinformatics, neuroscience, and structural biology, which involve data with inherent topological variations.