Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis

OAMF Alnaggar, BN Jagadale, MAN Saif… - Artificial Intelligence …, 2024 - Springer
In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray,
PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection …

A comprehensive survey on higher order neural networks and evolutionary optimization learning algorithms in financial time series forecasting

S Behera, SC Nayak, AVSP Kumar - Archives of Computational Methods …, 2023 - Springer
The financial market volatility has been a focus of study for experts over past decades. While
stockbrokers and investors expect reliable projections of future stock indices, it instead …

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

D Wolf, T Payer, CS Lisson, CG Lisson, M Beer… - Scientific Reports, 2023 - nature.com
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors,
reduce radiologist workload, and accelerate diagnosis. Training such deep learning models …

Industrial expert systems review: A comprehensive analysis of typical applications

X Yang, C Zhu - IEEE Access, 2024 - ieeexplore.ieee.org
As a branch of artificial intelligence (AI), expert systems are well-known for interpreting and
deducing solutions to problems based on the rules contained within a knowledge base …

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis

Q **ang, D Li, Z Hu, Y Yuan, Y Sun, Y Zhu, Y Fu… - Scientific Reports, 2024 - nature.com
Abstract The World Health Organization states that early diagnosis is essential to increasing
the cure rate for breast cancer, which poses a danger to women's health worldwide …

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Z Rudnicka, J Szczepanski, A Pregowska - Electronics, 2024 - mdpi.com
Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image
segmentation processes. Thus, the precise segmentation of organs and their lesions may …

Development a Novel Hybrid Deep Learning-Model for Brain Tumor Classification and Automated Diagnosis

S Gupta, V Bansla, S Kumar, G Singh… - 2024 International …, 2024 - ieeexplore.ieee.org
Tumors of the brain are among the deadliest diseases of the century. Artificial intelligence
(AI) and neuroscience are used to identify, identify, and categorize brain tumors. A large …

[HTML][HTML] Deep learning in pediatric neuroimaging

J Wang, J Wang, S Wang, Y Zhang - Displays, 2023 - Elsevier
The integration of deep learning techniques in pediatric neuroimaging has shown significant
promise in advancing various aspects of the field. This paper provides a comprehensive …

Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type

R Müller, M Duerschmidt, J Ullrich, C Knoll, S Weber… - Applied Sciences, 2024 - mdpi.com
Deep neural networks are powerful image classifiers but do they attend to similar image
areas as humans? While previous studies have investigated how this similarity is shaped by …

Deep learning-based blood cell classification from microscopic images for haematological disorder identification

NS Jagtap, V Bodade, V Kadrolli, H Mahajan… - Multimedia Tools and …, 2024 - Springer
Biomedicine struggles with blood-related disorders, although blood cells can reveal signs.
Automatic health diagnosis and monitoring using artificial intelligence (AI) can save time and …