The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI
Abstract The advent of Deep Learning (DL) has significantly propelled the field of diagnostic
radiology forward by enhancing image analysis and interpretation. The introduction of the …
radiology forward by enhancing image analysis and interpretation. The introduction of the …
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 …
Ensemble deep learning models for forecasting cryptocurrency time-series
Nowadays, cryptocurrency has infiltrated almost all financial transactions; thus, it is generally
recognized as an alternative method for paying and exchanging currency. Cryptocurrency …
recognized as an alternative method for paying and exchanging currency. Cryptocurrency …
[HTML][HTML] Adversarial attack and defence through adversarial training and feature fusion for diabetic retinopathy recognition
Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the
security and robustness of the deployed algorithms need to be guaranteed. The security …
security and robustness of the deployed algorithms need to be guaranteed. The security …
Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning
In this research work, a deep Convolutional Neural Network (CNN) was trained for image-
based Acoustic Emission (AE) waveform classification. AE waveforms from different damage …
based Acoustic Emission (AE) waveform classification. AE waveforms from different damage …
[HTML][HTML] Detecting glaucoma from fundus photographs using deep learning without convolutions: transformer for improved generalization
Purpose To compare the diagnostic accuracy and explainability of a Vision Transformer
deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained …
deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained …
Machine learning-based research for COVID-19 detection, diagnosis, and prediction: A survey
Y Meraihi, AB Gabis, S Mirjalili, A Ramdane-Cherif… - SN computer …, 2022 - Springer
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted
the whole world. The absence of treatment has motivated research in all fields to deal with it …
the whole world. The absence of treatment has motivated research in all fields to deal with it …
AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays
Artificial intelligence (AI) has played a significant role in image analysis and feature
extraction, applied to detect and diagnose a wide range of chest-related diseases. Although …
extraction, applied to detect and diagnose a wide range of chest-related diseases. Although …
[HTML][HTML] Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network
ST Seydi, M Hasanlou, J Chanussot - Ecological Indicators, 2022 - Elsevier
Accurate and timely mapping of wildfire burned areas is crucial for post-fire management,
planning, and next subsequent actions. The monitoring and mapping of the burned area by …
planning, and next subsequent actions. The monitoring and mapping of the burned area by …
AI-based pipeline for classifying pediatric medulloblastoma using histopathological and textural images
O Attallah, S Zaghlool - Life, 2022 - mdpi.com
Pediatric medulloblastomas (MBs) are the most common type of malignant brain tumors in
children. They are among the most aggressive types of tumors due to their potential for …
children. They are among the most aggressive types of tumors due to their potential for …