Pigmented skin lesion segmentation based on random forest and full convolutional neural networks

2018 ◽  
Author(s):  
Tie-Jun Yang ◽  
Shan Peng ◽  
Ping Hu ◽  
Lin Huang
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
Lesion Segmentation

Convolutional Neural Networks Applied for Skin Lesion Segmentation

2021 ◽  
Author(s):  
Graziela Silva Araujo ◽  
Guillermo Camara-Chavez ◽  
Roberta B. Oliveira
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
Lesion Segmentation

DermaDL: Advanced Convolutional Neural Networks for Computer-Aided Skin-Lesion Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Daniel M. Lima ◽  
Jose F. Rodrigues-Jr ◽  
Bruno Brandoli ◽  
Lorraine Goeuriot ◽  
Sihem Amer-Yahia
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
Computer Aided ◽  
Lesion Classification

scholarly journals Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

2020 ◽  
Vol 2 (1) ◽  
pp. 23-36
Author(s):  
Syed Aamir Ali Shah ◽  
Muhammad Asif Manzoor ◽  
Abdul Bais
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Forest Structure ◽  
State Of The Art ◽  
Spatial Association ◽  
Canopy Height ◽  
Landsat Images ◽  
Structure Estimation ◽  
Vegetation Height

Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

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