A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope
This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
transfer learning in the context of medical imaging. Medical imaging plays a critical role in …
Medical image data augmentation: techniques, comparisons and interpretations
E Goceri - Artificial Intelligence Review, 2023 - Springer
Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …
DACBT: Deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment
The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian
cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on …
cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on …
U-Net-based models towards optimal MR brain image segmentation
Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …
therefore, an automatic and generalized system to address this task is needed. Among all …
A survey of deep learning techniques based Parkinson's disease recognition methods employing clinical data
Parkinson's disease (PD) is a critical neurological ailment that affects millions of individuals
worldwide. A correct diagnosis of Parkinson's disease is required for effective treatment …
worldwide. A correct diagnosis of Parkinson's disease is required for effective treatment …
Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review
L Fang, W Hu, G Pan - International Journal of Biometeorology, 2024 - Springer
In recent years, there has been a rapid increase in the application of machine learning
methods about predicting the incidence of dengue fever. However, the predictive factors and …
methods about predicting the incidence of dengue fever. However, the predictive factors and …
Bridged-U-Net-ASPP-EVO and deep learning optimization for brain tumor segmentation
Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big
challenge due to the complexity of brain tumor tissues, and segmenting these tissues from …
challenge due to the complexity of brain tumor tissues, and segmenting these tissues from …
Federated fusion of magnified histopathological images for breast tumor classification in the internet of medical things
Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be
automated with the potential of Artificial Intelligence (AI). Deep learning models rely on large …
automated with the potential of Artificial Intelligence (AI). Deep learning models rely on large …
DEBCM: deep learning-based enhanced breast invasive ductal carcinoma classification model in IoMT healthcare systems
Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment
of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a …
of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a …
DVAEGMM: Dual variational autoencoder with gaussian mixture model for anomaly detection on attributed networks
A significant aspect of today's digital information is attributed networks, which combine
multiple node attributes with the basic network topology to extract knowledge. Anomaly …
multiple node attributes with the basic network topology to extract knowledge. Anomaly …