A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.

Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).

Next generation legged robot locomotion: A review on control techniques.

The next generation of autonomous-legged robots will herald a new era in the fields of manufacturing, healthcare, terrain exploration, and surveillance. We can expect significant progress in a number of industries, including inspection, search and rescue, elderly care, workplace safety, and nuclear decommissioning. Advanced legged robots are built with a state-of-the-art architecture that makes use of stereo vision and inertial measurement data to navigate unfamiliar and challenging terrains.

Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI.

GI abnormalities significantly increase mortality rates and impose considerable strain on healthcare systems, underscoring the essential requirement for rapid detection, precise diagnosis, and efficient strategic treatment. To develop a CAD system, this study aims to automatically classify GI disorders utilizing various deep learning methodologies. The proposed system features a three-stage lightweight architecture, consisting of a feature extractor using PSE-CNN, a feature selector employing PCA, and a classifier based on DELM.

Compact multiplex PCR device for HIV-1 and HIV-2 viral load determination from finger-prick whole blood in resource-limited settings.

The human immunodeficiency virus (HIV) remains a major global health concern for which accurate viral load monitoring is essential for the management of HIV infection. The advent of antiretroviral therapy (ART) has transformed once-fatal HIV disease into a manageable chronic condition that now makes the need for VL testing which aims to satisfy international suppression targets 95-95-95 al l the more essential. Therefore, considering the complexity and diversity of HIV infection, it is essential to develop rapid diagnostic technologies suitable for different clinical situations.

Development and validation of a portable device for lab-free versatile nucleic acid extraction.

Nucleic acid testing (NAT) has revolutionized diagnostics by providing precise, rapid, and scalable detection methods for diverse biological samples. These recent advancements satisfy the increasing demand for on-site diagnostics, yet sample preparation remains a significant bottleneck for achieving highly sensitive diagnostic assays. There is an unmet need for compatible, efficient, and lab-free sample preparation for point-of-care NAT.