A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …
dramatically changed the medical landscape. Medical centres have adopted AI applications …
[HTML][HTML] Glaucoma diagnosis in the era of deep learning: A survey
Glaucoma, a leading cause of irreversible blindness worldwide, poses significant diagnostic
challenges due to its reliance on subjective evaluation. Recent advances in computer vision …
challenges due to its reliance on subjective evaluation. Recent advances in computer vision …
[HTML][HTML] Enhancing lung abnormalities detection and classification using a Deep Convolutional Neural Network and GRU with explainable AI: A promising approach …
Accurate and timely detection and classification of lung abnormalities are crucial for effective
diagnosis and treatment planning. In recent years, Deep Learning (DL) techniques have …
diagnosis and treatment planning. In recent years, Deep Learning (DL) techniques have …
Emperor penguin optimization algorithm-and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus …
Feature selection is an important component of the machine learning domain, which selects
the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a …
the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a …
Numerical grad-cam based explainable convolutional neural network for brain tumor diagnosis
Since the start of the current century, artificial intelligence has gone through critical
advances improving the capabilities of intelligent systems. Especially machine learning has …
advances improving the capabilities of intelligent systems. Especially machine learning has …
[HTML][HTML] Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach
The purpose of this paper is to propose a novel hybrid framework for evaluating and
benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi …
benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi …
Machine learning methods for diagnosis of eye-related diseases: a systematic review study based on ophthalmic imaging modalities
Glaucoma, diabetic retinopathy, diabetic hypertension (DHR), Cataract, and age-related
macular degeneration are some of the most common and important retinal diseases. A …
macular degeneration are some of the most common and important retinal diseases. A …
AD-CAM: Enhancing interpretability of convolutional neural networks with a lightweight framework-from black box to glass box
In the realm of machine vision, the convolutional neural network (CNN) is a frequently used
and significant deep learning method. It is challenging to comprehend how predictions are …
and significant deep learning method. It is challenging to comprehend how predictions are …
Artificial intelligence in glaucoma: opportunities, challenges, and future directions
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various
complex problems related to many areas of healthcare including ophthalmology. AI …
complex problems related to many areas of healthcare including ophthalmology. AI …
[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …