A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
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 …
Breast cancer classification from histopathological images using patch-based deep learning modeling
Accurate detection and classification of breast cancer is a critical task in medical imaging
due to the complexity of breast tissues. Due to automatic feature extraction ability, deep …
due to the complexity of breast tissues. Due to automatic feature extraction ability, deep …
Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks
Melanocytes are skin cells that give color to the skin and form melanin color pigments. The
unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis …
unbalanced division and proliferation of these cells result in skin cancer. The early diagnosis …
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 …
OP-convNet: a patch classification-based framework for CT vertebrae segmentation
Accurate vertebrae segmentation from medical images plays an important role in clinical
tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease …
tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease …
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 …
Early stage detection of scoliosis using machine learning algorithms
P Shrestha, A Singh, R Garg, I Sarraf… - … , Analytics, Big Data …, 2021 - ieeexplore.ieee.org
Scoliosis is the most common disease which is mainly identified from patient spine X-ray
images. It is mainly diagnosed based on sideways curvature image modality. In scoliosis …
images. It is mainly diagnosed based on sideways curvature image modality. In scoliosis …