AIMC Topic: Image Processing, Computer-Assisted

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Hybrid feature optimized CNN for rice crop disease prediction.

Scientific reports
The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Adva...

Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.

Scientific reports
In recent times, severe acute malnutrition (SAM) in India is considered a serious issue as per UNICEF 2022 records. In that record, 35.5% of children under age 5 are stunted, 19.3% are wasted, and 32% are underweight. Malnutrition, defined as these t...

EViT: An Eagle Vision Transformer With Bi-Fovea Self-Attention.

IEEE transactions on cybernetics
Owing to advancements in deep learning technology, vision transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. Nonetheless, ViTs still face some challenges, such as high computational complexity and the absen...

Dynamic Graph Representation Learning for Spatio-Temporal Neuroimaging Analysis.

IEEE transactions on cybernetics
Neuroimaging analysis aims to reveal the information-processing mechanisms of the human brain in a noninvasive manner. In the past, graph neural networks (GNNs) have shown promise in capturing the non-Euclidean structure of brain networks. However, e...

Automatic Segmentation of Bone Graft in Maxillary Sinus via Distance Constrained Network Guided by Prior Anatomical Knowledge.

IEEE journal of biomedical and health informatics
Maxillary Sinus Lifting is a crucial surgical procedure for addressing insufficient alveolar bone mass andsevere resorption in dental implant therapy. To accurately analyze the geometry changesof the bone graft (BG) in the maxillary sinus (MS), it is...

LUCF-Net: Lightweight U-Shaped Cascade Fusion Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
The performance of modern U-shaped neural networks for medical image segmentation has been significantly enhanced by incorporating Transformer layers. Although Transformer architectures are powerful at extracting global information, its ability to ca...

CDAF-Net: A Contextual Contrast Detail Attention Feature Fusion Network for Low-Dose CT Denoising.

IEEE journal of biomedical and health informatics
Low-dose computed tomography (LDCT) is a specialized CT scan with a lower radiation dose than normal-dose CT. However, the reduced radiation dose can introduce noise and artifacts, affecting diagnostic accuracy. To enhance the LDCT image quality, we ...

Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.

Japanese journal of radiology
PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs...

A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST).

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical image segmentation is important for quantitative disease diagnosis and treatment but relies on accurate pixel-wise labels, which are costly, time-consuming, and require domain expertise. This work introduces MIST (MIxed supervision, Self, and...

Machine Learning-Based Computer Vision for Depth Camera-Based Physiotherapy Movement Assessment: A Systematic Review.

Sensors (Basel, Switzerland)
Machine learning-based computer vision techniques using depth cameras have shown potential in physiotherapy movement assessment. However, a comprehensive understanding of their implementation, effectiveness, and limitations remains needed. Following ...