11 citations · 18 across the 6 of their papers we have counts for
6 papers
MSA-DCNN: A Data-Efficient Multi-Scale Attention Deformable CNN for Medical Image Classification
Hamza Hussaini, Shahana Bano, Eyad Elyan +1
Existing deep learning methods perform well in medical image classification but struggle with multi-scale morphology and limited annotations due to fixed sampling and data-hungry t…
Multi-Stage Boundary-Aware Transformer Network for Action Segmentation in Untrimmed Surgical Videos
Rezowan Shuvo, M S Mekala, Eyad Elyan
Understanding actions within surgical workflows is critical for evaluating post-operative outcomes and enhancing surgical training and efficiency. Capturing and analyzing long sequ…
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies
Md Junayed Hasan, Eyad Elyan, Yijun Yan +2
Retrofitting and thermographic survey (TS) companies in Scotland collaborate with social housing providers to tackle fuel poverty. They employ ground-level infrared (IR) camera-bas…
Two layer Ensemble of Deep Learning Models for Medical Image Segmentation
Truong Dang, Tien Thanh Nguyen, John McCall +2
In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved stat…
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests
Khaled Fawagreh, Mohamad Medhat Gaber, Eyad Elyan
Random Forest (RF) is an ensemble classification technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and…
On Extreme Pruning of Random Forest Ensembles for Real-time Predictive Applications
Khaled Fawagreh, Mohamad Medhat Gaber, Eyad Elyan
Random Forest (RF) is an ensemble supervised machine learning technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its…