2 citations · 5 across the 5 of their papers we have counts for
5 papers
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images
Muhammad Azeem Aslam, Asim Naveed, Nisar Ahmed
Breast ultrasound imaging is a valuable tool for early breast cancer detection, but automated tumor segmentation is challenging due to inherent noise, variations in scale of lesion…
AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation
Asim Naveed, Syed S. Naqvi, Tariq M. Khan +3
In computer-aided diagnosis tools employed for skin cancer treatment and early diagnosis, skin lesion segmentation is important. However, achieving precise segmentation is challeng…
TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation
Shahzaib Iqbal, Tariq M. Khan, Syed S. Naqvi +2
Deep learning has shown great potential for automated medical image segmentation to improve the precision and speed of disease diagnostics. However, the task presents significant d…
LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation
Mufassir M. Abbasi, Shahzaib Iqbal, Asim Naveed +3
Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images. However, current m…
Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions
Asim Naveed, Syed S. Naqvi, Tariq M. Khan +1
Skin cancer holds the highest incidence rate among all cancers globally. The importance of early detection cannot be overstated, as late-stage cases can be lethal. Classifying skin…