scholarly journals Deep learning for historical books: classification of printing technology for digitized images

2021 ◽  
Author(s):  
Chanjong Im ◽  
Yongho Kim ◽  
Thomas Mandl
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
Technological Progress ◽  
Sampling Strategies ◽  
Mass Analysis ◽  
Printing Technology ◽  
The Past ◽  
Historical Images ◽  
Classification Of Images

AbstractPrinting technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

scholarly journals WATER DISCHARGE MEASURING INSTRUMENTS: AN UP TO DATE OVERVIEW

2021 ◽  
Vol 3 ◽  
pp. 116-123
Author(s):  
Artem Iukhno ◽  
Sergei Buzmakov ◽  
Alisa Zorina
Keyword(s):  
Technological Progress ◽  
Water Discharge ◽  
Channel Width ◽  
Measuring Instruments ◽  
Management Decisions ◽  
The Past ◽  
Different Types ◽  
Analytical Review ◽  
New Instruments

Technological progress could not but affect the sphere of hydrometric measurements. New instruments have been implemented to add to such traditional measuring instruments as mechanical current meters or to replace them. Over the past 20 years, the number of different types measuring instruments has increased dramatically. That is why the analytical review and classification of these devices are needed to help with making appropriate management decisions in the field of streamflow monitoring and surveys. The article presents the multivariable classification of measuring instruments, based on such factors as: morphology scaling (channel width and depth), measuring conditions (open, weed or ice-covered channel), logistical factor (mobile or stationary) and required accuracy. Characteristics of each type of measuring instruments were also considered and the limitations of their applicability were described. The results presented in the paper are expected to expand the horizons of approaches used for estimation of water discharge.

scholarly journals Interpretability of a Deep Learning Based Approach for the Classification of Skin Lesions into Main Anatomic Body Sites

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6048
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Andrzej Brodzicki ◽  
Bill Cassidy ◽  
Connah Kendrick ◽  
Moi Hoon Yap
Keyword(s):  
Deep Learning ◽  
Malignant Melanoma ◽  
Skin Lesions ◽  
The Body ◽  
Anatomic Site ◽  
The Past ◽  
Depth Analysis ◽  
The Face

Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks, including VGG19, ResNet50, Xception, DenseNet121, and EfficientNetB0, to calculate the features with an adjusted and densely connected classifier. Furthermore, we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.

Decoding of graphically encoded numerical digits using deep learning and edge detection techniques

2021 ◽  
pp. 1-8
Author(s):  
P. V. S. M. S. Kartik ◽  
Konjeti B. V. N. S. Sumanth ◽  
V. N. V. Sri Ram ◽  
G. Jeyakumar
Keyword(s):  
Deep Learning ◽  
Edge Detection ◽  
Graphical Representation ◽  
Training Data ◽  
Text And Image ◽  
Detection Techniques ◽  
Probability Prediction ◽  
Classification Of Images ◽  
Processing Techniques

The encoding of a message is the creation of the message. The decoding of a message is how people can comprehend, and decipher the message. It is a procedure of understanding and interpretation of coded data into a comprehensible form. In this paper, a self-created explicitly defined function for encoding numerical digits into graphical representation is proposed. The proposed system integrates deep learning methods to get the probabilities of digit occurrence and Edge detection techniques for decoding the graphically encoded numerical digits to numerical digits as text. The proposed system’s major objective is to take in an Image with digits encoded in graphical format and give the decoded stream of digits corresponding to the graph. This system also employs relevant pre-processing techniques to convert RGB to text and image to Canny image. Techniques such as Multi-Label Classification of images and Segmentation are used for getting the probability of occurrence. The dataset is created, on our own, that consists of 1000 images. The dataset has the training data and testing data in the proportion of 9 : 1. The proposed system was trained on 900 images and the testing was performed on 100 images which were ordered in 10 classes. The model has created a precision of 89% for probability prediction.

scholarly journals MRI Images, Brain Lesions and Deep Learning

2021 ◽  
Author(s):  
Darwin Castillo ◽  
Vasudevan Lakshminarayanan ◽  
María José Rodríguez-Álvarez
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Demyelinating Diseases ◽  
Clinical Environment ◽  
The Past ◽  
Demyelinating Lesions ◽  
Brain Image Analysis ◽  
Aided Diagnosis ◽  
Constant Growth

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

scholarly journals Identifying individuals with recent COVID-19 through voice classification using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pichatorn Suppakitjanusant ◽  
Somnuek Sungkanuparph ◽  
Thananya Wongsinin ◽  
Sirapong Virapongsiri ◽  
Nittaya Kasemkosin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Performance ◽  
The Other ◽  
Healthy Individuals ◽  
Model Accuracy ◽  
Subtle Change ◽  
Classification Of Images ◽  
A Prospective Study ◽  
Voice Classification

AbstractRecently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.

Understanding Image Classification Using TensorFlow Deep Learning - Convolution Neural Network

2019 ◽  
Vol 3 (2) ◽  
pp. 19-37
Author(s):  
Vinit Kumar Gunjan ◽  
Rashmi Pathak ◽  
Omveer Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Fine Tuning ◽  
Learning Technology ◽  
The Neural Network ◽  
Learning Technique ◽  
Classification Of Images ◽  
Initial Classification

This article describes how to establish the neural network technique for various image groupings in a convolution neural network (CNN) training. In addition, it also suggests initial classification results using CNN learning characteristics and classification of images from different categories. To determine the correct architecture, we explore a transfer learning technique, called Fine-Tuning of Deep Learning Technology, a dataset used to provide solutions for individually classified image-classes.

scholarly journals Automating the assessment of biofouling in images using expert agreement as a gold standard

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nathaniel J. Bloomfield ◽  
Susan Wei ◽  
Bartholomew A. Woodham ◽  
Peter Wilkinson ◽  
Andrew P. Robinson
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Gold Standard ◽  
Automated Analysis ◽  
Marine Species ◽  
Shipping Industry ◽  
Classification Of Images ◽  
Fuel Costs ◽  
Deep Learning Model

AbstractBiofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because it increases fuel costs and presents a biosecurity risk by providing a pathway for non-indigenous marine species to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply deep learning to automate the classification of images from in-water inspections to identify the presence and severity of fouling. We combined several datasets to obtain over 10,000 images collected from in-water surveys which were annotated by a group biofouling experts. We compared the annotations from three experts on a 120-sample subset of these images, and found that they showed 89% agreement (95% CI: 87–92%). Subsequent labelling of the whole dataset by one of these experts achieved similar levels of agreement with this group of experts, which we defined as performing at most 5% worse (p $$=$$ = 0.009–0.054). Using these expert labels, we were able to train a deep learning model that also agreed similarly with the group of experts (p $$=$$ = 0.001–0.014), demonstrating that automated analysis of biofouling in images is feasible and effective using this method.

Sign in / Sign up

Export Citation Format

Share Document