Prediction of COVID-19 from chest CT images using an ensemble of deep learning models

S Biswas, S Chatterjee, A Majee, S Sen, F Schwenker… - Applied Sciences, 2021 - mdpi.com
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 …

Multi-modal pain intensity assessment based on physiological signals: A deep learning perspective

P Thiam, H Hihn, DA Braun, HA Kestler… - Frontiers in …, 2021 - frontiersin.org
Traditional pain assessment approaches ranging from self-reporting methods, to
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

SD Roy, S Das, D Kar, F Schwenker, R Sarkar - Sensors, 2021 - mdpi.com
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 …

An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

A Baccouche, B Garcia-Zapirain, AS Elmaghraby - Scientific reports, 2022 - nature.com
A computer-aided diagnosis (CAD) system requires automated stages of tumor detection,
segmentation, and classification that are integrated sequentially into one framework to assist …

Exploring deep physiological models for nociceptive pain recognition

P Thiam, P Bellmann, HA Kestler, F Schwenker - Sensors, 2019 - mdpi.com
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 …

Ensemble of deep learning models for sleep apnea detection: an experimental study

D Mukherjee, K Dhar, F Schwenker, R Sarkar - Sensors, 2021 - mdpi.com
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 …

Advancing synthesis of decision tree-based multiple classifier systems: an approximate computing case study

M Barbareschi, S Barone, N Mazzocca - Knowledge and Information …, 2021 - Springer
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 …

Detection of Diseases in Pandemic: A Predictive Approach Using Stack Ensembling on Multi-Modal Imaging Data

R Mansoor, MA Shah, HA Khattak, S Mussadiq… - Electronics, 2022 - mdpi.com
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 …

Specialization in hierarchical learning systems: a unified information-theoretic approach for supervised, unsupervised and reinforcement learning

H Hihn, DA Braun - Neural Processing Letters, 2020 - Springer
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 …

An improved approach for initial stage detection of laryngeal cancer using effective hybrid features and ensemble learning method

JS Joseph, A Vidyarthi, VP Singh - Multimedia Tools and Applications, 2024 - Springer
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 …