Data preprocessing for heart disease classification: A systematic literature review
H Benhar, A Idri, JL Fernández-Alemán - Computer Methods and Programs …, 2020 - Elsevier
Context Early detection of heart disease is an important challenge since 17.3 million people
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
Deep learning in cardiology
P Bizopoulos, D Koutsouris - IEEE reviews in biomedical …, 2018 - ieeexplore.ieee.org
The medical field is creating large amount of data that physicians are unable to decipher
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …
[HTML][HTML] A survey of deep learning and its applications: a new paradigm to machine learning
Nowadays, deep learning is a current and a stimulating field of machine learning. Deep
learning is the most effective, supervised, time and cost efficient machine learning approach …
learning is the most effective, supervised, time and cost efficient machine learning approach …
[HTML][HTML] A survey of swarm and evolutionary computing approaches for deep learning
Deep learning (DL) has become an important machine learning approach that has been
widely successful in many applications. Currently, DL is one of the best methods of …
widely successful in many applications. Currently, DL is one of the best methods of …
Artificial intelligence in abdominal aortic aneurysm
Objective Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only
curative treatment relies on open or endovascular repair. The decision to treat relies on the …
curative treatment relies on open or endovascular repair. The decision to treat relies on the …
Deep learning and big data in healthcare: a double review for critical beginners
L Bote-Curiel, S Munoz-Romero, A Gerrero-Curieses… - Applied Sciences, 2019 - mdpi.com
In the last few years, there has been a growing expectation created about the analysis of
large amounts of data often available in organizations, which has been both scrutinized by …
large amounts of data often available in organizations, which has been both scrutinized by …
CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering
D Sharifrazi, R Alizadehsani… - Mathematical …, 2022 - researchers.mq.edu.au
Myocarditis is the form of an inflammation of the middle layer of the heart wall which is
caused by a viral infection and can affect the heart muscle and its electrical system. It has …
caused by a viral infection and can affect the heart muscle and its electrical system. It has …
[HTML][HTML] A novel transfer learning approach for the classification of histological images of colorectal cancer
EF Ohata, JVS Chagas, GM Bezerra… - The Journal of …, 2021 - Springer
Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is
identified by histopathological evaluations of microscopic images of the cancerous region …
identified by histopathological evaluations of microscopic images of the cancerous region …
Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique
Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost
importance to be able to efficiently diagnose these arrhythmias promptly. There exist many …
importance to be able to efficiently diagnose these arrhythmias promptly. There exist many …
A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis
Aims Pulmonary arterial hypertension (PAH) is a progressive condition with high mortality.
Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target …
Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target …