scholarly journals A systematic review on application of deep learning in digestive system image processing

2021 ◽  
Author(s):  
Huangming Zhuang ◽  
Jixiang Zhang ◽  
Fei Liao
Keyword(s):  
Systematic Review ◽  
Image Processing ◽  
Deep Learning ◽  
Digestive System

scholarly journals Deep Learning-based Models of Molecular Phenotypes for Predicting the Overall Survival in Cancer

2021 ◽  
pp. 218-234
Author(s):  
Gerardo Cazzato ◽  
Anjali Oak ◽  
Asim Mustafa Khan ◽  
. Jayesh
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Image Processing ◽  
Deep Learning ◽  
Cancer Survival ◽  
Primary Sources ◽  
Learning Models ◽  
Molecular Phenotypes ◽  
Cancerous Cells

Aims: The aim of the study is to justify the need of deep learning predictive model in obtaining molecular phenotypes of overall cancer survival. Study Design: The study is based on the secondary qualitative data analysis through usage of systematic review. Methodology: A qualitative study has been conducted to analyse the necessity of deep learning.  It also includes the need for deep learning models to obtain the imaging of the cancer cells. In the study, a detailed discussion on deep learning has been made. The analysis of the primary sources has been obtained by evaluating the quality of the resources in the study. The study also comprises of a thematic analysis that enlightens the benefits of deep learning. The study is based on the analysis of 14 primary research-based articles out of 112 quantitative articles and structuring of a systematic review from the collected data. Results: The morphological and physiological changes that occur in the cancerous cells have been clearly evaluated in the research. The result signifies the prediction can be made by implementing deep learning in terms of cancer survival. Advancements in terms of technology in the medical field can thus be improved with the help of the deep learning process. It states the advancements of the deep learning models that are helpful in predicting the model of cancer to determine survival rate. Conclusion: Deep learning is a process that is considered to be a subset of artificial intelligence. Deep learning programmes are meant to be performed for complex learning models. Although there is difference in the concept of deep learning and image processing still artificial intelligence brings both together so as to ensure better performance in image processing. The need for deep learning models has become invasive, and it helps to build a strong ground for cancer survival.

Deep Learning-Based Image Processing for Whitecaps on the Ocean Surface

2020 ◽  
Vol 76 (2) ◽  
pp. I_163-I_168
Author(s):  
Yukun WANG ◽  
Yuji SUGIHARA ◽  
Xianting ZHAO ◽  
Haruki NAKASHIMA ◽  
Osama ELJAMAL
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Ocean Surface

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Deep Learning Applied to Electroencephalogram Data in Mental Disorders: A systematic Review

2021 ◽  
pp. 108117
Author(s):  
Mateo de Bardeci ◽  
Cheng Teng Ip ◽  
Sebastian Olbrich
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Mental Disorders

scholarly journals Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

2021 ◽  
Vol 38 (2) ◽  
pp. 18-44 ◽  
Author(s):  
Vishal Monga ◽  
Yuelong Li ◽  
Yonina C. Eldar
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Signal And Image Processing

scholarly journals Deep learning for cephalometric landmark detection: systematic review and meta-analysis

2021 ◽  
Author(s):  
Falk Schwendicke ◽  
Akhilanand Chaurasia ◽  
Lubaina Arsiwala ◽  
Jae-Hong Lee ◽  
Karim Elhennawy ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Meta Analysis ◽  
High Accuracy ◽  
Risk Of Bias ◽  
Automated Detection ◽  
Reference Test ◽  
Landmark Detection ◽  
Future Studies ◽  
Using Data

Abstract Objectives Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data From 321 identified records, 19 studies (published 2017–2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7–93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (–0.581; 95 CI: –1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

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