Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Redefining radiology: a review of artificial intelligence integration in medical imaging
R Najjar - Diagnostics, 2023 - mdpi.com
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making
its foray into radiology, a move that is catalysing transformational shifts in the healthcare …
its foray into radiology, a move that is catalysing transformational shifts in the healthcare …
Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
Explanation-driven deep learning model for prediction of brain tumour status using MRI image data
Cancer research has seen explosive development exploring deep learning (DL) techniques
for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We …
for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We …
[HTML][HTML] Machine learning in microseismic monitoring
The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
Current challenges of implementing artificial intelligence in medical imaging
The idea of using artificial intelligence (AI) in medical practice has gained vast interest due
to its potential to revolutionise healthcare systems. However, only some AI algorithms are …
to its potential to revolutionise healthcare systems. However, only some AI algorithms are …
[HTML][HTML] Unraveling the ethical enigma: artificial intelligence in healthcare
M Jeyaraman, S Balaji, N Jeyaraman, S Yadav - Cureus, 2023 - ncbi.nlm.nih.gov
The integration of artificial intelligence (AI) into healthcare promises groundbreaking
advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and …
advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and …
MobileNetV1-based deep learning model for accurate brain tumor classification
Brain tumors are among the most dangerous diseases that lead to mortality after a period of
time from injury. Therefore, physicians and healthcare professionals are advised to make an …
time from injury. Therefore, physicians and healthcare professionals are advised to make an …
Federated learning for medical image analysis: A survey
Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …
the small sample size problem. Many recent studies suggest using multi-domain data …
Comparison of different deep learning architectures for synthetic CT generation from MR images
A Bahrami, A Karimian, H Arabi - Physica Medica, 2021 - Elsevier
Purpose Among the different available methods for synthetic CT generation from MR images
for the task of MR-guided radiation planning, the deep learning algorithms have and do …
for the task of MR-guided radiation planning, the deep learning algorithms have and do …