Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review
Background Artificial intelligence (AI) has served humanity in many applications since its
inception. Currently, it dominates the imaging field—in particular, image classification. The …
inception. Currently, it dominates the imaging field—in particular, image classification. The …
The present and future of deep learning in radiology
Abstract The advent of Deep Learning (DL) is poised to dramatically change the delivery of
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …
healthcare in the near future. Not only has DL profoundly affected the healthcare industry it …
Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm
Motivation Brain or central nervous system cancer is the tenth leading cause of death in men
and women. Even though brain tumour is not considered as the primary cause of mortality …
and women. Even though brain tumour is not considered as the primary cause of mortality …
Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review
D Painuli, S Bhardwaj - Computers in Biology and Medicine, 2022 - Elsevier
Being a second most cause of mortality worldwide, cancer has been identified as a perilous
disease for human beings, where advance stage diagnosis may not help much in …
disease for human beings, where advance stage diagnosis may not help much in …
Multi-level image thresholding using Otsu and chaotic bat algorithm
SC Satapathy, N Sri Madhava Raja… - Neural Computing and …, 2018 - Springer
Multi-level thresholding is a helpful tool for several image segmentation applications.
Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme …
Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme …
Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review
Abstract Background Artificial Intelligence (AI), in particular, machine learning (ML) has
shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) …
shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) …
Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm
Abstract Background and Objective Fatty Liver Disease (FLD)-a disease caused by
deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The …
deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The …
Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images
Gastrointestinal tract (GIT) infections such as ulcers, bleeding, polyps, Crohn's disease and
cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient …
cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient …
[HTML][HTML] Bias investigation in artificial intelligence systems for early detection of Parkinson's disease: A narrative review
Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on
medical attention and clinical signs. It is subjective and does not have a good prognosis …
medical attention and clinical signs. It is subjective and does not have a good prognosis …
[HTML][HTML] A novel block imaging technique using nine artificial intelligence models for COVID-19 disease classification, characterization and severity measurement in …
Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung
damage. Manual classification and characterization of COVID-19 may be biased depending …
damage. Manual classification and characterization of COVID-19 may be biased depending …