Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
Convolutional neural networks in medical image understanding: a survey
Imaging techniques are used to capture anomalies of the human body. The captured images
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
must be understood for diagnosis, prognosis and treatment planning of the anomalies …
Covid-19 image data collection: Prospective predictions are the future
Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline
patient diagnosis and management has become more pressing than ever. As one of the …
patient diagnosis and management has become more pressing than ever. As one of the …
[Retracted] Deep Neural Networks for Medical Image Segmentation
P Malhotra, S Gupta, D Koundal… - Journal of …, 2022 - Wiley Online Library
Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …
applications in the field of analysis of images, augmented reality, machine vision, and many …
Transfer learning techniques for medical image analysis: A review
Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
has enabled early diagnosis of medical conditions. Manual image analysis methods are …
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks
Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating
effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as …
effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as …
Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
[HTML][HTML] High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images
In this study, multiple lung diseases are diagnosed with the help of the Neural Network
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
Background Chest x-rays are widely used in clinical practice; however, interpretation can be
hindered by human error and a lack of experienced thoracic radiologists. Deep learning has …
hindered by human error and a lack of experienced thoracic radiologists. Deep learning has …
[HTML][HTML] Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images
Lung cancer (LC) remains a leading cause of death worldwide. Early diagnosis is critical to
protect innocent human lives. Computed tomography (CT) scans are one of the primary …
protect innocent human lives. Computed tomography (CT) scans are one of the primary …