Accelerating the image processing by the optimization strategy for deep learning algorithm DBN

Ying, Changtian; Huang, Zhen; Ying, Changyan

doi:10.1186/s13638-018-1255-6

Cited by 19 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step in deep learning‐based coffee disease detection is image capturing and pre‐processing 32,33 . For practical purposes, all images considered in this study are captured using android smartphones of varying image capturing quality.…”

Section: Methodsmentioning

confidence: 99%

“…The first step in deep learning-based coffee disease detection is image capturing and preprocessing. 32,33 For practical purposes, all images considered in this study are captured using android smartphones of varying image capturing quality. We highly recommend the images be taken in front of a white or light background for the proposed segmentation algorithm to work as intended.…”

Section: Image Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones

Waldamichael

Debelee

Ayano

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Ethiopia's coffee export accounts for about 34% of all exports for the budget year 2019/2020. Making it the 10th-largest coffee exporter in the world. Coffee diseases cause around 30% loss in production annually. In this paper, we propose an approach for the detection of four classes of coffee leaf diseases, Rust, Miner, Cercospora, and Phoma by using a fast Hue, Saturation, and Value (HSV) color space segmentation and a Mo-bileNetV2 architecture trained by transfer learning.The proposed HSV color segmentation algorithm constitutes of separating the leaf from the background and separating infected spots on the leaf by automatically finding the best threshold value for the Saturation (S) channel of the HSV color space. The algorithm was compared to the YCgCr and k-means algorithms, in terms of Mean Intersection Over Union and F1-Score.The proposed HSV segmentation algorithm outperformed these methods and achieved an MIoU score of 72.13% and an F1 score of 82.54%. The proposed algorithm also outperforms these methods in terms of execution time, taking on average 0.02 s per image for the segmentation of diseased spots from healthy leaf spots. Our MobileNetV2 classifier achieved a 96% average classification accuracy and 96% average precision. The segmentation accuracy and faster execution

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Image Pre-processingmentioning

confidence: 99%

Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones

Waldamichael

Debelee

Ayano

2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…Open-source code for a public-facing implementation has been made accessible to the public. So far, this approach has identified 36.1% of all confirmed positive cases in VA as part of the VA national response [ 43 , 44 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

Fatima

Shaikh²,

Riaz

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Context and Background: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and depression due to the disease and the present threat of an outbreak. Due to the fast propagation of the virus and misleading/fake information, the issues of public discourse alter, resulting in significant confusion in certain places. Rumours are unproven facts or stories that propagate and promote sentiments of prejudice, hatred, and fear. Objective. The study’s objective is to propose a novel solution to detect fake news using state-of-the-art machines and deep learning models. Furthermore, to analyse which models outperformed in detecting the fake news. Method. In the research study, we adapted a COVID-19 rumours dataset, which incorporates rumours from news websites and tweets, together with information about the rumours. It is important to analyse data utilizing Natural Language Processing (NLP) and Deep Learning (DL) approaches. Based on the accuracy, precision, recall, and the f1 score, we can assess the effectiveness of the ML and DL algorithms. Results. The data adopted from the source (mentioned in the paper) have collected 9200 comments from Google and 34,779 Twitter postings filtered for phrases connected with COVID-19-related fake news. Experiment 1. The dataset was assessed using the following three criteria: veracity, stance, and sentiment. In these terms, we have different labels, and we have applied the DL algorithms separately to each term. We have used different models in the experiment such as (i) LSTM and (ii) Temporal Convolution Networks (TCN). The TCN model has more performance on each measurement parameter in the evaluated results. So, we have used the TCN model for the practical implication for better findings. Experiment 2. In the second experiment, we have used different state-of-the-art deep learning models and algorithms such as (i) Simple RNN; (ii) LSTM + Word Embedding; (iii) Bidirectional + Word Embedding; (iv) LSTM + CNN-1D; and (v) BERT. Furthermore, we have evaluated the performance of these models on all three datasets, e.g., veracity, stance, and sentiment. Based on our second experimental evaluation, the BERT has a superior performance over the other models compared.

show abstract

“…The first successful application came with AlexNet’s 13 breakthrough performance on the ImageNet Large Scale Visual Recognition Competition in 2012. Since then, numerous CNN architectures have been proposed, with notable contributions to the field including VGGNet 14 , ResNet 15 , Inception V3 16 , and DBN for image processing 17 . Medical image-based classification has specifically benefited from more performant computer vision techniques.…”

Section: Introductionmentioning

confidence: 99%

Automated human cell classification in sparse datasets using few-shot learning

Walsh

Abdelpakey

Shehata

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techniques. In practice, a large amount of data is required to accurately train these deep learning models. However, due to the sparse human cell datasets currently available, the performance of these models is typically low. This study investigates the feasibility of using few-shot learning-based techniques to mitigate the data requirements for accurate training. The study is comprised of three parts: First, current state-of-the-art few-shot learning techniques are evaluated on human cell classification. The selected techniques are trained on a non-medical dataset and then tested on two out-of-domain, human cell datasets. The results indicate that, overall, the test accuracy of state-of-the-art techniques decreased by at least 30% when transitioning from a non-medical dataset to a medical dataset. Reptile and EPNet were the top performing techniques tested on the BCCD dataset and HEp-2 dataset respectively. Second, this study evaluates the potential benefits, if any, to varying the backbone architecture and training schemes in current state-of-the-art few-shot learning techniques when used in human cell classification. To this end, the best technique identified in the first part of this study, EPNet, is used for experimentation. In particular, the study used 6 different network backbones, 5 data augmentation methodologies, and 2 model training schemes. Even with these additions, the overall test accuracy of EPNet decreased from 88.66% on non-medical datasets to 44.13% at best on the medical datasets. Third, this study presents future directions for using few-shot learning in human cell classification. In general, few-shot learning in its current state performs poorly on human cell classification. The study proves that attempts to modify existing network architectures are not effective and concludes that future research effort should be focused on improving robustness towards out-of-domain testing using optimization-based or self-supervised few-shot learning techniques.

show abstract

Accelerating the image processing by the optimization strategy for deep learning algorithm DBN

Cited by 19 publications

References 13 publications

Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones

Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

Automated human cell classification in sparse datasets using few-shot learning

Contact Info

Product

Resources

About