[HTML][HTML] Glaucoma diagnosis in the era of deep learning: A survey
Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic
challenges due to its reliance on subjective evaluation. Recent advances in computer vision …
challenges due to its reliance on subjective evaluation. Recent advances in computer vision …
Deep learning and computer vision for glaucoma detection: A review
Glaucoma is the leading cause of irreversible blindness worldwide and poses significant
diagnostic challenges due to its reliance on subjective evaluation. However, recent …
diagnostic challenges due to its reliance on subjective evaluation. However, recent …
Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs
MM Dehshibi, T Olugbade… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
There is a growing body of studies on applying deep learning to biometrics analysis. Certain
circumstances, however, could impair the objective measures and accuracy of the proposed …
circumstances, however, could impair the objective measures and accuracy of the proposed …
Acoustic identification of ae. aegypti mosquitoes using smartphone apps and residual convolutional neural networks
In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solutions
for raising awareness among the population on the proliferation of Aedes aegypti …
for raising awareness among the population on the proliferation of Aedes aegypti …
ADVISE: ADaptive feature relevance and VISual Explanations for convolutional neural networks
To equip convolutional neural networks (CNNs) with explainability, it is essential to interpret
how opaque models make specific decisions, understand what causes the errors, improve …
how opaque models make specific decisions, understand what causes the errors, improve …
Incorporating reinforcement learning for quality-aware sample selection in deep architecture training
G Adhane, MM Dehshibi… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Many samples are necessary to train a convolutional neural network (CNN) to achieve
optimum performance while maintaining generalizability. Several studies, however, have …
optimum performance while maintaining generalizability. Several studies, however, have …
Contributions to explainable deep learning models
G Adhane - 2024 - openaccess.uoc.edu
In this work, we propose techniques to enhance the performance and transparency of
convolutional neural networks (CNNs). We introduce novel methods for informative sample …
convolutional neural networks (CNNs). We introduce novel methods for informative sample …