AI & Machine Learning

Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy

26 sec read61 views0 listens

Researchers have developed a transformer-based model using RT-DETR architecture to accurately detect fungal hyphae in KOH microscopy images for diagnosing dermatophytosis, achieving high precision and recall rates while distinguishing between fungal elements and artefacts. This AI system can serve as an automated screening tool, enhancing the reliability of image-level analysis and aiding clinical decision-making for content creators focusing on medical diagnostics.

Read the full article at arXiv cs.CV (Vision)

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.

You Might Like

AI & Machine LearningApr 271m & 2 s read

Olumide: A self-hosted AI nurse for chronic disease, living in WhatsApp

The project described is an innovative healthcare solution leveraging artificial intelligence and IoT to assist elderly patients, specifically focusing on chronic disease management for hypertensive and diabetic individuals in Nigeria. Here's a summa...

Ali Nemati

AI & Machine LearningApr 1425 sec read

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering

Researchers at arXiv have introduced MedLVR, a new framework for medical visual question answering (VQA) that enhances accuracy by incorporating iterative visual reasoning during autoregressive decoding. This approach addresses limitations in existin...

alinemati1983-6987

AI & Machine LearningApr 13, 202227 sec read

Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow

Researchers developed a low-code named entity recognition workflow using Prodigy to quickly create datasets for training models that can automatically identify clinical procedures in unstructured text from electronic health records. This advancement ...

Ali Nemati