[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …
developments in deep neural networks have contributed to significant advances in medical …
Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision
S Asif, Y Wenhui, S ur-Rehman, Q ul-ain… - … Methods in Engineering, 2024 - Springer
Abstract Machine learning (ML) has emerged as a versatile and powerful tool in various
fields of medicine, revolutionizing early disease diagnosis, particularly in cases where …
fields of medicine, revolutionizing early disease diagnosis, particularly in cases where …
En-MinWhale: An ensemble approach based on MRMR and Whale optimization for Cancer diagnosis
According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for~
10 million fatalities at the end of 2020. The most common types of cancers include Lung …
10 million fatalities at the end of 2020. The most common types of cancers include Lung …
Smart carbon-based sensors for the detection of non-coding RNAs associated with exposure to micro (nano) plastics: an artificial intelligence perspective
Abstract Micro (nano) plastics (MNPs) are pervasive environmental pollutants that
individuals eventually consume. Despite this, little is known about MNP's impact on public …
individuals eventually consume. Despite this, little is known about MNP's impact on public …
Supervised feature selection on gene expression microarray datasets using manifold learning
M Zare, N Azizizadeh, A Kazemipour - Chemometrics and Intelligent …, 2023 - Elsevier
In recent decades, the ultimate output from microarray assay, has produced enormous
numbers of microarray datasets, regardless of the used technology. These datasets include …
numbers of microarray datasets, regardless of the used technology. These datasets include …
[HTML][HTML] Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data
SR Oshternian, S Loipfinger, A Bhattacharya… - BMC …, 2024 - Springer
Background Numerous transcriptomic-based models have been developed to predict or
understand the fundamental mechanisms driving biological phenotypes. However, few …
understand the fundamental mechanisms driving biological phenotypes. However, few …
[HTML][HTML] Dimensionality reduction for images of IoT using machine learning
I Ali, K Wassif, H Bayomi - Scientific Reports, 2024 - nature.com
Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are
becoming increasingly integrated into all aspects of our lives. They are capable of gathering …
becoming increasingly integrated into all aspects of our lives. They are capable of gathering …
[HTML][HTML] Alzheimer's disease detection using data fusion with a deep supervised encoder
Alzheimer's disease (AD) is affecting a growing number of individuals. As a result, there is a
pressing need for accurate and early diagnosis methods. This study aims to achieve this …
pressing need for accurate and early diagnosis methods. This study aims to achieve this …
[HTML][HTML] Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification
Gene expression data are usually known for having a large number of features. Usually,
some of these features are irrelevant and redundant. However, in some cases, all features …
some of these features are irrelevant and redundant. However, in some cases, all features …
[HTML][HTML] Principles of artificial intelligence in radiooncology
Purpose In the rapidly expanding field of artificial intelligence (AI) there is a wealth of
literature detailing the myriad applications of AI, particularly in the realm of deep learning …
literature detailing the myriad applications of AI, particularly in the realm of deep learning …