scholarly journals Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2353
Author(s):  
Rolando Miragaia ◽  
Francisco Chávez ◽  
Josefa Díaz ◽  
Antonio Vivas ◽  
Maria Henar Prieto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Cost Savings ◽  
Light Conditions ◽  
Ripening Stage ◽  
Significant Cost ◽  
Technological Transformation ◽  
The Status ◽  
Acquisition Method

Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness ,three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.

scholarly journals Image Analysis using Deep Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1385-1388
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Techniques ◽  
Massive Information ◽  
Comparative Examination ◽  
Hypothetical Structure ◽  
The Way

In late years, critical learning methodologies especially Convolutional Neural Networks have been utilized in different solicitations. CNN's have appeared to be a key capacity to ordinarily expel broad volumes of data from massive information. The uses of CNNs have inside and out ended up being useful especially in orchestrating ordinary pictures. Regardless, there have been essential obstacles in executing the CNNs in a restorative zone as a result of the nonattendance of genuine getting ready data. Consequently, general imaging benchmarks, for instance, Image Net have been conspicuously used in the restorative not too zone notwithstanding the way that they are perfect when appeared differently about the CNNs. In this paper, a comparative examination of LeNet, AlexNet, and GoogLeNet has been done. Starting there, the paper has proposed an improved hypothetical structure for requesting helpful life structures pictures using CNNs. In perspective on the proposed structure of the framework, the CNNs building are required to beat the previous three plans in requesting remedial pictures.

scholarly journals A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Amr Abd-Elrahman ◽  
Katie Britt ◽  
Vance Whitaker
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Image Analysis ◽  
Deep Learning ◽  
Geographic Information System ◽  
Precision Agriculture ◽  
System Analysis ◽  
Plant Canopy ◽  
Geographic Information System Analysis ◽  
Agriculture Management

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

scholarly journals Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenic Alopecia

2021 ◽  
Author(s):  
Meng Gao ◽  
Yue Wang ◽  
Haipeng Xu ◽  
Congcong Xu ◽  
Xianhong Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Quantitative Model ◽  
High Accuracy ◽  
Diameter Distribution ◽  
Androgenic Alopecia ◽  
Hair Density ◽  
Learning Framework ◽  
Quantitative Indicators

Since the results of basic and specific classification in male androgenic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. The aim of this study was to develop a deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images, and a quantitative model for predicting basic and specific classification in male androgenic alopecia with high accuracy.

scholarly journals Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

2017 ◽  
Author(s):  
Chuangqi Wang ◽  
Shawn Kang ◽  
Eunice Kim ◽  
Xitong Zhang ◽  
Hee June Choi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Cell Migration ◽  
Deep Learning ◽  
Computational Analysis ◽  
Time Lapse ◽  
Collective Cell Migration ◽  
Edge Detector ◽  
Cell Protrusion ◽  
Canny Edge

AbstractCell protrusion plays important roles in cell migration by pushing plasma membrane forward. Cryptic lamellipodia induce the protrusion of submarginal cells in collective cell migration where cells are attached and move together. Although computational image analysis of cell protrusion has been done extensively, the study on protrusion activities of cryptic lamellipodia is limited due to difficulties in image segmentation. This study seeks to aid in the computational analysis of submarginal cell protrusion in collective cell migration by using deep learning to detect the cryptic lamellipodial edges from fluorescence time-lapse movies. Due to the noisy features within overlapping cells, the conventional image analysis algorithms such as Canny edge detector and intensity thresholding are limited. In this paper we combined Canny edge detector, Deep Neural Networks (DNNs), and local intensity thresholding. We were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies of PtK1 cells using both simple convolutional neural networks and VGG-16 based neural networks. We used relatively small effort to prepare the training set to train the DNNs to detect the cryptical lamellipodial edges in fluorescence time-lapse movies. This work demonstrates that deep learning can be combined with the conventional image analysis algorithms to facilitate the computational analysis of highly complex time-lapse movies of collective cell migration.

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

2021 ◽  
Author(s):  
◽  
Mahdieh Shabanian ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Neurological Disease ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Disease State ◽  
Institutional Review Board ◽  
Institutional Review

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases.

scholarly journals The Zombification of Art History

2019 ◽  
Vol 11 (2) ◽  
pp. 28-35
Author(s):  
Tsila Hassine ◽  
Ziv Neeman
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Art History ◽  
The Past ◽  
New Directions ◽  
Artistic Image

In the past few years deep-learning AI neural networks have achieved major milestones in artistic image analysis and generation, producing what some refer to as ‘art.’ We reflect critically on some of the artistic shortcomings of a few projects that occupied the spotlight in recent years. We introduce the term ‘Zombie Art’ to describe the generation of new images of dead masters, as well as ‘The AI Reproducibility Test.’ We designate the problems inherent in AI and in its application to art history. In conclusion, we propose new directions for both AI-generated art and art history, in the light of these new powerful AI technologies of artistic image analysis and generation.

scholarly journals A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2132
Author(s):  
Kyriakos D. Apostolidis ◽  
George A. Papakostas
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Language Processing ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Attack Detection ◽  
Detection Methods ◽  
Proper Function ◽  
Magnetic Resonance Imaging Mri

In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to face numerous challenging problems. One of the most important challenges in the CV area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper function of these models can significantly upgrade the health systems. However, recent studies have shown that CNN models are vulnerable under adversarial attacks with imperceptible perturbations. In this paper, we summarize existing methods for adversarial attacks, detections and defenses on medical imaging. Finally, we show that many attacks, which are undetectable by the human eye, can degrade the performance of the models, significantly. Nevertheless, some effective defense and attack detection methods keep the models safe to an extent. We end with a discussion on the current state-of-the-art and future challenges.

scholarly journals Medical Imaging using Deep Learning Models

2021 ◽  
Vol 6 (5) ◽  
pp. 156-167
Author(s):  
Chetanpal Singh
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Potential Role ◽  
Medical Image Analysis ◽  
Learning Models ◽  
Quality Healthcare ◽  
Learning Techniques

Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.

scholarly journals Does Two-Class Training Extract Real Features? A COVID-19 Case Study

2021 ◽  
Vol 11 (4) ◽  
pp. 1424
Author(s):  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Francisco Luna-Perejón ◽  
Manuel Domínguez-Morales ◽  
Antón Civit
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Image Classification ◽  
Health Professionals ◽  
Classification System ◽  
Learning Systems ◽  
Healthy Patient ◽  
X Ray

Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms.

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