Medical image identification methods: A review
J Li, P Jiang, Q An, GG Wang, HF Kong - Computers in Biology and …, 2024 - Elsevier
The identification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Medical image data mainly include electronic health …
medical image retrieval and mining. Medical image data mainly include electronic health …
A review of video-based human activity recognition: theory, methods and applications
Video-based human activity recognition (HAR) is an important task in many fields, such as
healthcare monitoring, video surveillance, and sports analysis. This review paper aims to …
healthcare monitoring, video surveillance, and sports analysis. This review paper aims to …
Deep learning empowered breast cancer diagnosis: Advancements in detection and classification
Recent advancements in AI, driven by big data technologies, have reshaped various
industries, with a strong focus on data-driven approaches. This has resulted in remarkable …
industries, with a strong focus on data-driven approaches. This has resulted in remarkable …
[HTML][HTML] A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images
K Jabeen, MA Khan, MA Hameed, O Alqahtani… - Frontiers in …, 2024 - frontiersin.org
With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and
mortality rate of this disease pose severe global health issues for women. Identifying the …
mortality rate of this disease pose severe global health issues for women. Identifying the …
XAI-RACapsNet: Relevance aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation
Breast cancer is a malignant condition characterized by the uncontrolled growth of abnormal
cells in breast tissues, often forming a lump or mass that can be detected through screening …
cells in breast tissues, often forming a lump or mass that can be detected through screening …
MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms
Abstract Background and objective Deep Learning models have emerged as a significant
tool in generating efficient solutions for complex problems including cancer detection, as …
tool in generating efficient solutions for complex problems including cancer detection, as …
Wrist-based electrodermal activity monitoring for stress detection using federated learning
With the most recent developments in wearable technology, the possibility of continually
monitoring stress using various physiological factors has attracted much attention. By …
monitoring stress using various physiological factors has attracted much attention. By …
Harris-Hawk-Optimization-Based Deep Recurrent Neural Network for Securing the Internet of Medical Things
The healthcare industry has recently shown much interest in the Internet of Things (IoT). The
Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances …
Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances …
A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction
Computed tomography (CT) is used in a wide range of medical imaging diagnoses.
However, the reconstruction of CT images from raw projection data is inherently complex …
However, the reconstruction of CT images from raw projection data is inherently complex …
Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks
H Hoang Luong, H Thanh Nguyen… - Journal of Information …, 2024 - Taylor & Francis
Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that
affects many people around the world, especially since it is the most deadly cancer in …
affects many people around the world, especially since it is the most deadly cancer in …