Prediction of COVID-19 from chest CT images using an ensemble of deep learning models
The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease,
COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of …
COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of …
Multi-modal pain intensity assessment based on physiological signals: A deep learning perspective
Traditional pain assessment approaches ranging from self-reporting methods, to
observational scales, rely on the ability of an individual to accurately assess and …
observational scales, rely on the ability of an individual to accurately assess and …
Computer aided breast cancer detection using ensembling of texture and statistical image features
Breast cancer, like most forms of cancer, is a fatal disease that claims more than half a
million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly …
million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly …
An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks
A computer-aided diagnosis (CAD) system requires automated stages of tumor detection,
segmentation, and classification that are integrated sequentially into one framework to assist …
segmentation, and classification that are integrated sequentially into one framework to assist …
Exploring deep physiological models for nociceptive pain recognition
Standard feature engineering involves manually designing measurable descriptors based
on some expert knowledge in the domain of application, followed by the selection of the best …
on some expert knowledge in the domain of application, followed by the selection of the best …
Ensemble of deep learning models for sleep apnea detection: an experimental study
Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from
this disease. In-time diagnosis of apnea is needed which can be observed by the application …
this disease. In-time diagnosis of apnea is needed which can be observed by the application …
Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study
So far, multiple classifier systems have been increasingly designed to take advantage of
hardware features, such as high parallelism and computational power. Indeed, compared to …
hardware features, such as high parallelism and computational power. Indeed, compared to …
Detection of Diseases in Pandemic: A Predictive Approach Using Stack Ensembling on Multi-Modal Imaging Data
Deep Learning (DL) in Medical Imaging is an emerging technology for diagnosing various
diseases, ie, pneumonia, lung cancer, brain stroke, breast cancer, etc. In Machine Learning …
diseases, ie, pneumonia, lung cancer, brain stroke, breast cancer, etc. In Machine Learning …
Specialization in hierarchical learning systems: a unified information-theoretic approach for supervised, unsupervised and reinforcement learning
Joining multiple decision-makers together is a powerful way to obtain more sophisticated
decision-making systems, but requires to address the questions of division of labor and …
decision-making systems, but requires to address the questions of division of labor and …
An improved approach for initial stage detection of laryngeal cancer using effective hybrid features and ensemble learning method
Squamous cell carcinoma (SCC) is one of the most common as well as deadliest kinds of
laryngeal cancer. The precise and early identification of laryngeal cancer plays a pivotal role …
laryngeal cancer. The precise and early identification of laryngeal cancer plays a pivotal role …