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Found 11 papers, 1 papers with code
LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation
By focusing on the retinal blood vessels, we were able to thoroughly analyze the performance and effectiveness of the LMBiS-Net model.
Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation
Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for the tracking and diagnosis of progression.
Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions
Skin cancer holds the highest incidence rate among all cancers globally.
LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms
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.
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness.
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
Image segmentation is an important task in medical imaging.
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction
Compression of convolutional neural network models has recently been dominated by pruning approaches.
A fast and accurate iris segmentation method using an LoG filter and its zero-crossings
In the proposed method, an LoG filter with region growing is used to detect the pupil region.
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening
The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening.
Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation
Two common examples are downsampling of the input images and reducing the network depth to meet computer memory constraints.
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons.
