A
Adnan Haider
Researcher at Dongguk University
Publications - 6
Citations - 60
Adnan Haider is an academic researcher from Dongguk University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 6 publications receiving 8 citations.
Papers
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Proceedings ArticleDOI
Exploiting Residual Edge Information in Deep Fully Convolutional Neural Networks For Retinal Vessel Segmentation
Tariq M. Khan,Syed S. Naqvi,Muhammad Arsalan,Muhamamd Aurangzeb Khan,Haroon Ahmed Khan,Adnan Haider +5 more
TL;DR: A new supervised method using a variant of the fully convolutional neural network is pro-posed with the advantages of reduced hyper-parameters, reduced computational/memory requirements, and robust performance in capturing tiny vessel information.
Journal ArticleDOI
Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data
TL;DR: In this paper, an optimal multilevel deep-aggregated boosted network was proposed to recognize COVID-19 infection from heterogeneous radiographic data, including X-ray and CT images.
Journal ArticleDOI
Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database
TL;DR: In this paper, a light-weighted deep network is proposed, which is the first ensemble design (based on MobileNet, ShuffleNet, and FCNet) in medical domain (particularly for COVID19 diagnosis) that encompasses the reduced number of trainable parameters.
Journal ArticleDOI
Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine.
Tahir Mahmood,Muhammad Owais,Kyoung Jun Noh,Hyo Sik Yoon,Ja Hyung Koo,Adnan Haider,Haseeb Sultan,Kang Ryoung Park +7 more
TL;DR: Wang et al. as mentioned in this paper adopted a new nuclear segmentation network empowered by residual skip connections to solve the problem of manual inspection of histopathology images under high-resolution microscopes.
Journal ArticleDOI
Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine.
TL;DR: Wang et al. as mentioned in this paper proposed a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients.