no code implementations • 17 Dec 2023 • Tariq M Khan, Syed S. Naqvi, Erik Meijering
Segmentation is an important task in a wide range of computer vision applications, including medical image analysis.
1 code implementation • 10 Sep 2023 • Mufassir M. Abbasi, Shahzaib Iqbal, Asim Naveed, Tariq M. Khan, Syed S. Naqvi, Wajeeha Khalid
By focusing on the retinal blood vessels, we were able to thoroughly analyze the performance and effectiveness of the LMBiS-Net model.
no code implementations • 11 Jun 2023 • Asim Naveed, Syed S. Naqvi, Tariq M. Khan, Imran Razzak
Skin cancer holds the highest incidence rate among all cancers globally.
no code implementations • 9 Jun 2023 • Shahzaib Iqbal, Tariq M. Khan, Syed S. Naqvi, Muhammad Usman, Imran Razzak
The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms.
no code implementations • 25 Apr 2023 • Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly, Imran Razzak
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness.
no code implementations • 14 Oct 2022 • Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly, Erik Meijering
Compression of convolutional neural network models has recently been dominated by pruning approaches.
no code implementations • 16 Jan 2022 • Malik A. Manan, Tariq M. Khan, Ahsan Saadat, Muhammad Arsalan, Syed S. Naqvi
The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening.
no code implementations • 21 Dec 2021 • Tariq M Khan, Antonio Robles-Kelly, Syed S. Naqvi
Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets.
no code implementations • 21 Dec 2021 • Tariq M. Khan, Syed S. Naqvi, Erik Meijering
Two common examples are downsampling of the input images and reducing the network depth to meet computer memory constraints.