Patient-derived xenograft models in cancer therapy: technologies and applications
Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted
into immunocompromised or humanized mice, have shown superiority in recapitulating the …
into immunocompromised or humanized mice, have shown superiority in recapitulating the …
Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
Deep learning for intrusion detection and security of Internet of things (IoT): current analysis, challenges, and possible solutions
In the last decade, huge growth is recorded globally in computer networks and Internet of
Things (IoT) networks due to the exponential data generation, approximately zettabyte to a …
Things (IoT) networks due to the exponential data generation, approximately zettabyte to a …
Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray
Background and objective Automatic segmentation and annotation of medical image plays a
critical role in scientific research and the medical care community. Automatic segmentation …
critical role in scientific research and the medical care community. Automatic segmentation …
Bone fracture detection using deep supervised learning from radiological images: A paradigm shift
Bone diseases are common and can result in various musculoskeletal conditions (MC). An
estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from …
estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from …
BrainNet: optimal deep learning feature fusion for brain tumor classification
Early detection of brain tumors can save precious human life. This work presents a fully
automated design to classify brain tumors. The proposed scheme employs optimal deep …
automated design to classify brain tumors. The proposed scheme employs optimal deep …
Deep versus handcrafted tensor radiomics features: prediction of survival in head and neck cancer using machine learning and fusion techniques
Background: Although handcrafted radiomics features (RF) are commonly extracted via
radiomics software, employing deep features (DF) extracted from deep learning (DL) …
radiomics software, employing deep features (DF) extracted from deep learning (DL) …
Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network
Purpose For radiologists, identifying and assessing thelung nodules of cancerous form from
CT scans is a difficult and laborious task. As a result, early lung growing prediction is …
CT scans is a difficult and laborious task. As a result, early lung growing prediction is …
[HTML][HTML] Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images
Objective Tuberculosis (TB) is among the most frequent causes of infectious-disease-related
mortality. Despite being treatable by antibiotics, tuberculosis often goes misdiagnosed and …
mortality. Despite being treatable by antibiotics, tuberculosis often goes misdiagnosed and …
Current applications of deep learning and radiomics on CT and CBCT for maxillofacial diseases
The increasing use of computed tomography (CT) and cone beam computed tomography
(CBCT) in oral and maxillofacial imaging has driven the development of deep learning and …
(CBCT) in oral and maxillofacial imaging has driven the development of deep learning and …