Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

G Krishnan, S Singh, M Pathania, S Gosavi… - Frontiers in Artificial …, 2023 - frontiersin.org
As the demand for quality healthcare increases, healthcare systems worldwide are
grappling with time constraints and excessive workloads, which can compromise the quality …

Developments in image processing using deep learning and reinforcement learning

J Valente, J António, C Mora, S Jardim - Journal of Imaging, 2023 - mdpi.com
The growth in the volume of data generated, consumed, and stored, which is estimated to
exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for …

ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition

R Ranjbarzadeh, S Jafarzadeh Ghoushchi… - Artificial Intelligence …, 2023 - Springer
Breast tumor segmentation and recognition from mammograms play a key role in healthcare
and treatment services. As different tumors in mammography have dissimilar densities …

[HTML][HTML] A review on brain tumor segmentation based on deep learning methods with federated learning techniques

MF Ahamed, MM Hossain, M Nahiduzzaman… - … Medical Imaging and …, 2023 - Elsevier
Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …

Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm

R Ranjbarzadeh, P Zarbakhsh, A Caputo… - Computers in Biology …, 2024 - Elsevier
Reliable and accurate brain tumor segmentation is a challenging task even with the
appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic …

Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data

AA Joshi, RM Aziz - International Journal of Imaging Systems …, 2024 - Wiley Online Library
This study addresses the critical challenge of accurately classifying brain tumors using
artificial intelligence. Early detection is crucial, as untreated tumors can be fatal. Despite …

[HTML][HTML] Vision transformers in multi-modal brain tumor MRI segmentation: A review

P Wang, Q Yang, Z He, Y Yuan - Meta-Radiology, 2023 - Elsevier
Brain tumors have shown extreme mortality and increasing incidence during recent years,
which bring enormous challenges for the timely diagnosis and effective treatment of brain …

A deep learning model for ergonomics risk assessment and sports and health monitoring in self-occluded images

A Aghamohammadi, SA Beheshti Shirazi… - Signal, Image and Video …, 2024 - Springer
Ergonomic assessments and sports and health monitoring play a crucial role and have
contributed to sustainable development in many areas such as product architecture, design …

Understanding EEG signals for subject-wise definition of armoni activities

K Raj, A Singh, A Mandal, T Kumar, AM Roy - arXiv preprint arXiv …, 2023 - arxiv.org
In a growing world of technology, psychological disorders became a challenge to be solved.
The methods used for cognitive stimulation are very conventional and based on one-way …

Brain tumor segmentation based on zernike moments, enhanced ant lion optimization, and convolutional neural network in MRI images

A Bagherian Kasgari, R Ranjbarzadeh… - … and Optimization in …, 2023 - Springer
Gliomas that form in glial cells in the spinal cord and brain are the most aggressive and
common kinds of brain tumors (intra-axial brain tumors) due to their rapid progression and …