MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641086058 A) filed by Vallurupalli Nageswara Rao Vignana Jyothi Institute Of Engineering Technology on July 14, 2026, for Artificial Intelligence Framework For Early Monkeypox Diagnosis Through Dermatological Image Classification.
Inventors include Ch Suresh Kumar Raju; Shaik Saddam Hussain; Vijaya Bhaskara Reddy V; G. Laxmi Deepthi; Vijayakumar Chilamkurthi; and Karumuri Sri Rama Murthy.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Monkeypox virus infections in humans typically arise through direct contact with infected animals whether via bites, scratches, or handling of contaminated bodily fluids and can initially mimic the clinical presentation of chickenpox, featuring fever, malaise, and a vesicular-pustular rash. While many patients experience a self-limiting course with lesions resolving over two to four weeks, a subset may develop life-threatening complications such as widespread bacterial sepsis, bronchopneumonia, or encephalitis if the virus breaches deeper tissues or secondary infections take hold. Given the overlap of early symptoms with more common dermatologic conditions and the potential for rapid escalation to severe illness, heightened vigilance among clinicians and prompt laboratory confirmation are indispensable for outbreak containment and optimal patient outcomes. To facilitate timely and accurate differentiation of monkeypox from other exanthematous diseases, we propose an automated skin-lesion classification framework harnessing both convolutional neural networks (CNNs) .We evaluate a suite of leading CNN backbones ResNet, VGG19, MobileNet, and Inception-ResNet for dermatologic imagery. To render the deep learning decisions interpretable, Grad-CAM heatmaps highlight salient regions driving each prediction. Performance is rigorously assessed through accuracy, precision, recall, F1-score, and ROC- AUC metrics, demonstrating the potential of this pipeline to support clinicians in early detection and differential diagnosis of emerging viral exanthems.
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