Adnan Haider
Papers
3
Total Citations
52
H-Index
3
About
Adnan Haider is a leading researcher in medical image analysis and agricultural computer vision, specializing in deep learning architectures for multi-scale feature extraction. His most impactful work addresses critical challenges in colorectal cancer diagnosis, where his 2023 paper on multi-scale feature retention and aggregation for gastrointestinal images (26 citations) demonstrates how computer-assisted diagnosis can enhance robot-assisted minimally invasive surgery by improving lesion detection accuracy. Haider extends his expertise to sustainable agriculture through his 2024 work on multi-scale, multi-receptive field feature fusion for plant disease and fruit segmentation (15 citations), offering automated solutions that replace time-consuming manual assessment methods. His earlier foundational research on accurate pixel-wise skin segmentation using shallow fully convolutional neural networks (11 citations) has broad applications in human activity recognition, video surveillance, and robotic surgery. Across these domains, Haider consistently advances automated visual understanding, achieving significant citation impact by developing robust, efficient models that bridge the gap between complex medical and agricultural imaging tasks. His work is particularly notable for its practical deployment potential in clinical and field settings.
Research Focus
Key Achievements
Top Papers
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