Muhammad Adnan Khan

Vision–language foundation models for multimodal medical image understanding and report generation: a narrative review with quantitative synthesis

Vision-language foundation models (VLFMs) for medical image understanding have advanced rapidly, yet evidence supporting clinical deployment remains fragmented. This narrative review synthesizes architectures, training paradigms, benchmarks, public datasets, and translation barriers. Drawing on more than 50 original st...

Deep Learning-Driven Sustainable Strategies for Potato Leaf Disease Detection to Enhance Crop Yield and Soil Fertility

Potato plants are very important in world food security but also have a high susceptibility to diseases like early blight and late blight, which increase crop loss and hamper stable production practices. In this study, an early potato leaf disease detection method using deep learning is proposed, apply transfer-learnin...

Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.

Intelligent DDoS Attack Detection in Software-Defined Networks Using Explainable Machine Learning

An explainable machine learning-based framework for accurate, transparent, and reliable DDoS attack detection in an SDN environment that combines reliable DDoS detection with transparent, analyst-oriented decision support for SDN security monitoring is developed.

Visible–Infrared Image Fusion for Computer Vision: A Review of Datasets and Fusion Strategies in Object Detection and Facial-Expression Recognition

The need for better-aligned multimodal datasets, standardized evaluation protocols, lightweight fusion architectures, and robust models capable of operating in dynamic real-world environments is highlighted.