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Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization …
Today, diagnostic systems based on artificial intelligence play a significant role in confirming
doctors' recommendations. These systems are becoming effective tools in clinical treatment …
doctors' recommendations. These systems are becoming effective tools in clinical treatment …
Develo** deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy
This study presents a novel dynamic localisation-based decision (DLBD) with fuzzy
weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic …
weighting with zero inconsistency (FWZIC) under a probabilistic single-valued neutrosophic …
Smart home management system with face recognition based on ArcFace model in deep convolutional neural network
TV Dang - Journal of Robotics and Control (JRC), 2022 - journal.umy.ac.id
Hybrid archimedes sine cosine optimization enabled deep learning for multilevel brain tumor classification using mri images
The most terrible form of cancer caused by uncontrolled and aberrant cell division is the
Brain Tumor (BT). The current methodologies are insufficient for precise categorization due …
Brain Tumor (BT). The current methodologies are insufficient for precise categorization due …
Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation
Z Wang, D Zhao, AA Heidari, Y Chen, H Chen… - Scientific Reports, 2024 - nature.com
Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-
threshold image segmentation methods are favored for their computational simplicity and …
threshold image segmentation methods are favored for their computational simplicity and …
A turning point few-shot learning for COVID-19 diagnosis
L Qain, Y Bouteraa, T Vaiyapuri, Y Haung - Engineering Applications of …, 2024 - Elsevier
The current landscape of medical diagnostics grapples with a critical challenge posed by the
limitations of existing meta-learning techniques in interpreting complex representations from …
limitations of existing meta-learning techniques in interpreting complex representations from …
ResUNet++: a comprehensive improved UNet++ framework for volumetric semantic segmentation of brain tumor MR images
Predictive models in radiology can now be constructed with remarkable accuracy using an
amalgamation of radiomics and artificial intelligence. To effectively prepare for surgical …
amalgamation of radiomics and artificial intelligence. To effectively prepare for surgical …
Mobile Diagnosis of COVID-19 by Biogeography-based Optimization-guided CNN
X Han, Z Hu - Mobile Networks and Applications, 2024 - Springer
Abstract Since 2019, COVID-19 has profoundly impacted human health around the world.
COVID-19 is extremely contagious, so fast automated diagnosis is necessary. In the field of …
COVID-19 is extremely contagious, so fast automated diagnosis is necessary. In the field of …
Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification
Accurate classification of logos is a challenging task in image recognition due to variations
in logo size, orientation, and background complexity. Deep learning models, such as …
in logo size, orientation, and background complexity. Deep learning models, such as …
Enhancing anomaly detection Efficiency: Introducing grid searchbased multi-population particle Swarm optimization algorithm based optimized Regional based …
Anomaly detection is critically important for enhancing data security across networks,
industrial applications, and fraud detection systems. Traditional methods in anomaly …
industrial applications, and fraud detection systems. Traditional methods in anomaly …