MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning
Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …
strong need for automatic medical image segmentation. Still, current image segmentation …
Skin lesion segmentation using fully convolutional networks: A comparative experimental study
Because the most dangerous type of skin cancer, melanoma, is very difficult for
dermatologists to detect because of the low contrast between the lesion and the adjacent …
dermatologists to detect because of the low contrast between the lesion and the adjacent …
Research related to the diagnosis of prostate cancer based on machine learning medical images: A review
X Chen, X Liu, Y Wu, Z Wang, SH Wang - International journal of medical …, 2023 - Elsevier
Background Prostate cancer is currently the second most prevalent cancer among men.
Accurate diagnosis of prostate cancer can provide effective treatment for patients and greatly …
Accurate diagnosis of prostate cancer can provide effective treatment for patients and greatly …
Automated chest ct image segmentation of covid-19 lung infection based on 3d u-net
The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has
a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of …
a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of …
[HTML][HTML] Robust chest CT image segmentation of COVID-19 lung infection based on limited data
Background The coronavirus disease 2019 (COVID-19) affects billions of lives around the
world and has a significant impact on public healthcare. For quantitative assessment and …
world and has a significant impact on public healthcare. For quantitative assessment and …
Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review
The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare,
addressing critical challenges in securing electronic health records (EHRs), ensuring data …
addressing critical challenges in securing electronic health records (EHRs), ensuring data …
Unsupervised X-ray image segmentation with task driven generative adversarial networks
Semantic parsing of anatomical structures in X-ray images is a critical task in many clinical
applications. Modern methods leverage deep convolutional networks, and generally require …
applications. Modern methods leverage deep convolutional networks, and generally require …
Thyroid disorder analysis using random forest classifier
Nowadays, diseases are increasing due to the lifestyle of human beings. Thyroid disorder is
also increasing. There are two types of thyroid disorder. Hyperthyroidism occurs due to the …
also increasing. There are two types of thyroid disorder. Hyperthyroidism occurs due to the …
Segmentation quality assessment by automated detection of erroneous surface regions in medical images
Despite the advancement in deep learning-based semantic segmentation methods, which
have achieved accuracy levels of field experts in many computer vision applications, the …
have achieved accuracy levels of field experts in many computer vision applications, the …
Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation
Z Wang, D Zhao, AA Heidari, Y Chen, H Chen… - Scientific Reports, 2024 - nature.com
Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-
threshold image segmentation methods are favored for their computational simplicity and …
threshold image segmentation methods are favored for their computational simplicity and …