Early Detection of Melanoma in Dermoscopy of Skin Lesion Images by Computer Vision–Based System

2015 ◽  
pp. 361-400
Keyword(s):  
Computer Vision ◽  
Skin Lesion ◽  
Early Detection

scholarly journals Analytical Review of Major Nocturnal Pests’ Detection Technique using Computer Vision

2018 ◽  
Vol 11 (3) ◽  
pp. 179-182
Author(s):  
Deven J. Patel ◽  
Nirav Bhatt
Keyword(s):  
Computer Vision ◽  
Early Detection ◽  
Review Article ◽  
Pink Bollworm ◽  
Detection Technique ◽  
Future Research ◽  
White Grub ◽  
Analytical Review ◽  
Pest Detection ◽  
The Right

Research in agriculture is increasing quality and quantity, but pest reduces it. To prevent the effect of these pests, insecticides are used. But excessive use of pesticides is very harmful to production and environment. So initially pest detection is necessary. We work on nocturnal pests because that can be easily attracting using night trapping tools. The purpose of this review article is to analyse the popular techniques and find the right technique for the initial diagnosis and early detection of major nocturnal flying pests like Pink Bollworm, White Grub, Helicoverpa and Spodoptera. The importance of early detection can be in identifying and classifying the pests in a digital view. We have concluded our results with the various methods and the prospects of future research.

scholarly journals Multimodal Multi-tasking for Skin Lesion Classification Using Deep Neural Networks

2021 ◽  
pp. 27-38
Author(s):  
Rafaela Carvalho ◽  
João Pedrosa ◽  
Tudor Nedelcu
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Classification Performance ◽  
Additional Information ◽  
Deep Learning Model ◽  
Lesion Classification

AbstractSkin cancer is one of the most common types of cancer and, with its increasing incidence, accurate early diagnosis is crucial to improve prognosis of patients. In the process of visual inspection, dermatologists follow specific dermoscopic algorithms and identify important features to provide a diagnosis. This process can be automated as such characteristics can be extracted by computer vision techniques. Although deep neural networks can extract useful features from digital images for skin lesion classification, performance can be improved by providing additional information. The extracted pseudo-features can be used as input (multimodal) or output (multi-tasking) to train a robust deep learning model. This work investigates the multimodal and multi-tasking techniques for more efficient training, given the single optimization of several related tasks in the latter, and generation of better diagnosis predictions. Additionally, the role of lesion segmentation is also studied. Results show that multi-tasking improves learning of beneficial features which lead to better predictions, and pseudo-features inspired by the ABCD rule provide readily available helpful information about the skin lesion.

Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD

2020 ◽  
Vol 4 (2) ◽  
pp. 151-173 ◽  
Author(s):  
Mai S. Mabrouk ◽  
Ahmed Y. Sayed ◽  
Heba M. Afifi ◽  
Mariam A. Sheha ◽  
Amr Sharwy
Keyword(s):  
Skin Lesion ◽  
Early Detection

Validation of a Skin-Lesion Image-Matching Algorithm Based on Computer Vision Technology

2016 ◽  
Vol 22 (1) ◽  
pp. 45-50 ◽  
Author(s):  
Raymond H. Chen ◽  
Magnus Snorrason ◽  
Shelley M. Enger ◽  
Eslam Mostafa ◽  
Justin M. Ko ◽  
...  
Keyword(s):  
Computer Vision ◽  
Skin Lesion ◽  
Image Matching ◽  
Matching Algorithm ◽  
Computer Vision Technology

Using imagery and computer vision as remote monitoring methods for early detection of respiratory disease in pigs

2021 ◽  
Vol 187 ◽  
pp. 106283
Author(s):  
Maria Jorquera-Chavez ◽  
Sigfredo Fuentes ◽  
Frank R. Dunshea ◽  
Robyn D. Warner ◽  
Tomas Poblete ◽  
...  
Keyword(s):  
Computer Vision ◽  
Early Detection ◽  
Respiratory Disease ◽  
Remote Monitoring ◽  
Monitoring Methods
Sign in / Sign up

Export Citation Format

Share Document