scholarly journals Optimized Convolutional Neural Network Models for Skin Lesion Classification

2022 ◽  
Vol 70 (2) ◽  
pp. 2131-2148
Author(s):  
Juan Pablo Villa-Pulgarin ◽  
Anderson Alberto Ruales-Torres ◽  
Daniel Arias-Garz髇 ◽  
Mario Alejandro Bravo-Ortiz ◽  
Harold Brayan Arteaga-Arteaga ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Lesion Classification

Robustness of convolutional neural network models in hyperspectral noisy datasets with loss functions

2021 ◽  
Vol 90 ◽  
pp. 107009
Author(s):  
Sepehr Ghafari ◽  
Milad Ghobadi Tarnik ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Loss Functions ◽  
Neural Network Models

scholarly journals Epoch interrogation for skin cancer detection using convolutional neural network models

2021 ◽  
Vol 1074 (1) ◽  
pp. 012025
Author(s):  
A Poornima ◽  
M Shyamala Devi ◽  
M Sumithra ◽  
Mullaguri Venkata Bharath ◽  
Swathi ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Network Models ◽  
Neural Network Models ◽  
Skin Cancer Detection

scholarly journals Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

2021 ◽  
Author(s):  
Robert J. O’Shea ◽  
Amy Rose Sharkey ◽  
Gary J. R. Cook ◽  
Vicky Goh
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Model Performance ◽  
Network Models ◽  
Imaging Studies ◽  
Neural Network Models ◽  
Eligibility Criteria ◽  
Data Partitions

Abstract Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. Key Points • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

scholarly journals Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation

2020 ◽  
Author(s):  
Haoti Zhong ◽  
Cong Liao ◽  
Anna Cinzia Squicciarini ◽  
Sencun Zhu ◽  
David Miller
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models

scholarly journals U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales

2021 ◽  
Vol 104 ◽  
pp. 102510
Author(s):  
Karim Malik ◽  
Colin Robertson ◽  
Douglas Braun ◽  
Clara Greig
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models

Feature extraction-based image steganalysis using deep learning

2021 ◽  
pp. 188-198
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Secure Communication ◽  
Information Technologies ◽  
Network Models ◽  
Error Rates ◽  
Multimedia Data ◽  
Secret Message ◽  
Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

Chest X-Ray Image Analysis of Convolutional Neural Network Models with Transfer Learning for Prediction of COVID Patients

2021 ◽  
pp. 353-361
Author(s):  
M. Shyamala Devi ◽  
P. Swathi ◽  
N. Pavan Kumar ◽  
Ravi Varma Tungala ◽  
Saranya Vivekanandan ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Network Models ◽  
Neural Network Models ◽  
X Ray ◽  
Chest X Ray
Sign in / Sign up

Export Citation Format

Share Document