A survey on deep learning applied to medical images: from simple artificial neural networks to generative models
P Celard, EL Iglesias, JM Sorribes-Fdez… - Neural Computing and …, 2023 - Springer
Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …
in medical image analysis. This paper surveys fundamental deep learning concepts related …
Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation
The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-
driven infrastructure that highly relies on smart sensing devices (ie, blood pressure monitors …
driven infrastructure that highly relies on smart sensing devices (ie, blood pressure monitors …
Breast cancer diagnosis from histopathology images using deep neural network and XGBoost
Abstract Background and Objective: Globally, breast cancer is one of the most common
diseases among women. As a result of the disadvantages of manual analysis, computer …
diseases among women. As a result of the disadvantages of manual analysis, computer …
[HTML][HTML] Small and overlapping worker detection at construction sites
Although there has been study on worker detection using computer vision (CV) for the safety
of construction sites, it is still challenging to identify employees who are obstructed or have …
of construction sites, it is still challenging to identify employees who are obstructed or have …
AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease
Objective We develop a deep learning framework based on the pre-trained Bidirectional
Encoder Representations from Transformers (BERT) model using unstructured clinical notes …
Encoder Representations from Transformers (BERT) model using unstructured clinical notes …
An attention-guided convolutional neural network for automated classification of brain tumor from MRI
Early diagnosis of brain tumor using magnetic resonance imaging (MRI) is vital for timely
medication and effective treatment. But, most people living in remote areas do not have …
medication and effective treatment. But, most people living in remote areas do not have …
[HTML][HTML] A novel image expression-driven modeling strategy for coke quality prediction in the smart cokemaking process
Y Qiu, Y Hui, P Zhao, CH Cai, B Dai, J Dou… - Energy, 2024 - Elsevier
In pursuit of carbon neutrality and advancing energy-efficient practices within the steel and
coking industries, the traditional cokemaking process is progressively evolving towards …
coking industries, the traditional cokemaking process is progressively evolving towards …
[HTML][HTML] An enhanced CNN-LSTM based multi-stage framework for PV and load short-term forecasting: DSO scenarios
The importance of accurate forecasting in the electric sector has grown due to the increasing
demand and adoption of high volume of Renewable Energy Sources (RES). Short-term …
demand and adoption of high volume of Renewable Energy Sources (RES). Short-term …
[HTML][HTML] An improved SqueezeNet model for the diagnosis of lung cancer in CT scans
M Tsivgoulis, T Papastergiou… - Machine Learning with …, 2022 - Elsevier
Lung cancer is the leading cause of cancer deaths nowadays and its early detection and
treatment plays an important role in survival of patients. The main challenge is to acquire an …
treatment plays an important role in survival of patients. The main challenge is to acquire an …
Land-use and land-cover classification in semi-arid areas from medium-resolution remote-sensing imagery: A deep learning approach
K Ali, BA Johnson - Sensors, 2022 - mdpi.com
Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, eg,
urban/rural planning, disaster management, and climate change adaptation. Recently, Deep …
urban/rural planning, disaster management, and climate change adaptation. Recently, Deep …