A survey on artificial intelligence in pulmonary imaging
Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer
vision and image recognition creating widespread opportunities of using artificial …
vision and image recognition creating widespread opportunities of using artificial …
CT‐based automatic spine segmentation using patch‐based deep learning
CT vertebral segmentation plays an essential role in various clinical applications, such as
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …
computer‐assisted surgical interventions, assessment of spinal abnormalities, and vertebral …
Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis
The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes
a lot of time and effort to label lesions, automatic segmentation methods have to be created …
a lot of time and effort to label lesions, automatic segmentation methods have to be created …
A lightweight convolutional neural network model for liver segmentation in medical diagnosis
Liver segmentation and recognition from computed tomography (CT) images is a warm topic
in image processing which is helpful for doctors and practitioners. Currently, many deep …
in image processing which is helpful for doctors and practitioners. Currently, many deep …
SVseg: Stacked sparse autoencoder-based patch classification modeling for vertebrae segmentation
Precise vertebrae segmentation is essential for the image-related analysis of spine
pathologies such as vertebral compression fractures and other abnormalities, as well as for …
pathologies such as vertebral compression fractures and other abnormalities, as well as for …
[Retracted] Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning
M Ahmad, SF Qadri, MU Ashraf, K Subhi… - Computational …, 2022 - Wiley Online Library
Segmentation of a liver in computed tomography (CT) images is an important step toward
quantitative biomarkers for a computer‐aided decision support system and precise medical …
quantitative biomarkers for a computer‐aided decision support system and precise medical …
Recent advancements and future prospects in active deep learning for medical image segmentation and classification
Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
COVID-19 severity detection using chest X-ray segmentation and deep learning
COVID-19 has resulted in a significant global impact on health, the economy, education, and
daily life. The disease can range from mild to severe, with individuals over 65 or those with …
daily life. The disease can range from mild to severe, with individuals over 65 or those with …
EdgeSVDNet: 5G-enabled detection and classification of vision-threatening diabetic retinopathy in retinal fundus images
The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for
advanced and efficient early detection mechanisms. With the integration of the Internet of …
advanced and efficient early detection mechanisms. With the integration of the Internet of …
Fully automatic liver and tumor segmentation from CT image using an AIM-Unet
The segmentation of the liver is a difficult process due to the changes in shape, border, and
density that occur in each section in computed tomography (CT) images. In this study, the …
density that occur in each section in computed tomography (CT) images. In this study, the …