Deep multimodal fusion for semantic image segmentation: A survey
Recent advances in deep learning have shown excellent performance in various scene
understanding tasks. However, in some complex environments or under challenging …
understanding tasks. However, in some complex environments or under challenging …
The Matthews correlation coefficient (MCC) is more informative than Cohen's Kappa and Brier score in binary classification assessment
Even if measuring the outcome of binary classifications is a pivotal task in machine learning
and statistics, no consensus has been reached yet about which statistical rate to employ to …
and statistics, no consensus has been reached yet about which statistical rate to employ to …
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images
History shows that the infectious disease (COVID-19) can stun the world quickly, causing
massive losses to health, resulting in a profound impact on the lives of billions of people …
massive losses to health, resulting in a profound impact on the lives of billions of people …
Thermal object detection in difficult weather conditions using YOLO
M Krišto, M Ivasic-Kos, M Pobar - IEEE access, 2020 - ieeexplore.ieee.org
Global terrorist threats and illegal migration have intensified concerns for the security of
citizens, and every effort is made to exploit all available technological advances to prevent …
citizens, and every effort is made to exploit all available technological advances to prevent …
Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region
G Karayegen, MF Aksahin - Biomedical Signal Processing and Control, 2021 - Elsevier
When it comes to medical image segmentation on brain MR images, using deep learning
techniques has a significant impact to predict tumor existence. Manual segmentation of a …
techniques has a significant impact to predict tumor existence. Manual segmentation of a …
Boundary loss for remote sensing imagery semantic segmentation
A Bokhovkin, E Burnaev - International Symposium on Neural Networks, 2019 - Springer
In response to the growing importance of geospatial data, its analysis including semantic
segmentation becomes an increasingly popular task in computer vision today. Convolutional …
segmentation becomes an increasingly popular task in computer vision today. Convolutional …
Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study
SM Badawy, AENA Mohamed, AA Hefnawy, HE Zidan… - PloS one, 2021 - journals.plos.org
Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast
facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep …
facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep …
[HTML][HTML] Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset
YH Nai, BW Teo, NL Tan, S O'Doherty… - Computers in biology …, 2021 - Elsevier
Nine previously proposed segmentation evaluation metrics, targeting medical relevance,
accounting for holes, and added regions or differentiating over-and under-segmentation …
accounting for holes, and added regions or differentiating over-and under-segmentation …
The algorithm of watershed color image segmentation based on morphological gradient
Y Wu, Q Li - Sensors, 2022 - mdpi.com
The traditional watershed algorithm has the disadvantage of over-segmentation and
interference with an image by reflected light. We propose an improved watershed color …
interference with an image by reflected light. We propose an improved watershed color …
Automatic mapping of center pivot irrigation systems from satellite images using deep learning
The availability of freshwater is becoming a global concern. Because agricultural
consumption has been increasing steadily, the mapping of irrigated areas is key for …
consumption has been increasing steadily, the mapping of irrigated areas is key for …