[HTML][HTML] Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward
The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …
networks has significantly progressed and advanced the field of computer vision (CV) and …
Bias and class imbalance in oncologic data—towards inclusive and transferrable AI in large scale oncology data sets
Simple Summary Large-scale medical data carries significant areas of underrepresentation
and bias at all levels: clinical, biological, and management. Resulting data sets and outcome …
and bias at all levels: clinical, biological, and management. Resulting data sets and outcome …
An efficient deep learning-based skin cancer classifier for an imbalanced dataset
Efficient skin cancer detection using images is a challenging task in the healthcare domain.
In today's medical practices, skin cancer detection is a time-consuming procedure that may …
In today's medical practices, skin cancer detection is a time-consuming procedure that may …
AI fairness in data management and analytics: A review on challenges, methodologies and applications
P Chen, L Wu, L Wang - Applied Sciences, 2023 - mdpi.com
This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …
(AI) systems, delving into its background, definition, and development process. The article …
Edge AI for early detection of chronic diseases and the spread of infectious diseases: opportunities, challenges, and future directions
E Badidi - Future Internet, 2023 - mdpi.com
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge
devices, is quickly becoming a critical component in early health prediction. Edge AI …
devices, is quickly becoming a critical component in early health prediction. Edge AI …
An empirical evaluation of sampling methods for the classification of imbalanced data
M Kim, KB Hwang - PLoS One, 2022 - journals.plos.org
In numerous classification problems, class distribution is not balanced. For example, positive
examples are rare in the fields of disease diagnosis and credit card fraud detection. General …
examples are rare in the fields of disease diagnosis and credit card fraud detection. General …
An efficient deep learning model to detect COVID-19 using chest X-ray images
The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome
coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted …
coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted …
Exploring key spatio-temporal features of crash risk hot spots on urban road network: A machine learning approach
Traffic safety is a critical factor that has always been considered in policy making for urban
transportation planning and management. Accurately predicting crash risk hot spots allows …
transportation planning and management. Accurately predicting crash risk hot spots allows …
An intelligent correlation learning system for person Re-identification
Person re-identification (PRe-id) aims to retrieve a target person's images captured across
multiple/single non-overlap** cameras. To this end, significant techniques have been …
multiple/single non-overlap** cameras. To this end, significant techniques have been …
Wearable IMU-based human activity recognition algorithm for clinical balance assessment using 1D-CNN and GRU ensemble model
YW Kim, KL Joa, HY Jeong, S Lee - Sensors, 2021 - mdpi.com
In this study, a wearable inertial measurement unit system was introduced to assess patients
via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose …
via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose …