Industrial image classification using a randomized neural-net ensemble and feedback mechanism

2016 ◽  
Vol 173 ◽  
pp. 708-714 ◽  
Author(s):  
Weitao Li ◽  
Keqiong Chen ◽  
Dianhui Wang
Keyword(s):  
Image Classification ◽  
Feedback Mechanism ◽  
Neural Net

A Shallow Neural Net for Retinal OCT Image Classification

2021 ◽  
pp. 519-529
Author(s):  
Mahua Nandy Pal ◽  
Minakshi Banerjee ◽  
Ankit Sarkar
Keyword(s):  
Image Classification ◽  
Neural Net ◽  
Oct Image

scholarly journals AIDeveloper: deep learning image classification in life science and beyond

2020 ◽  
Author(s):  
Martin Kräter ◽  
Shada Abuhattum ◽  
Despina Soteriou ◽  
Angela Jacobi ◽  
Thomas Krüger ◽  
...  
Keyword(s):  
Blood Cell ◽  
Cell Count ◽  
Image Classification ◽  
Whole Blood ◽  
Classification Performance ◽  
Neural Nets ◽  
Neural Net ◽  
Label Free ◽  
Blood Cell Count ◽  
Free Classification

AbstractPublications on artificial intelligence (AI)-based image analysis have increased drastically in recent years. However, all applications use individual solutions highly specialized for a particular task. Here, we present an easy-to-use, adaptable, open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming. The software provides a variety of NN-architectures that can be simply selected for training. AID allows the user to apply trained models on new data, obtain metrics for classification performance, and export final models to different formats. The working principles of AID are first illustrated by training a convolutional neural net (CNN) on a large dataset consisting of images of different objects (CIFAR-10). We further explore the potential of AID by training a model to distinguish areas of differentiated and non-differentiated mesenchymal stem cells (MSCs) in culture. Additionally, we compare a conventional clinical whole blood cell count with a whole blood cell count performed by an NN-trained, using a dataset of more than 1.2 million images obtained by real-time deformability cytometry, delivering comparable results. Finally, we demonstrate how AID can be used for label-free classification of B- and T-cells derived from human blood, which currently requires costly and time-consuming sample preparation. Thus, AID can empower anyone to develop, train, and apply NNs for image classification. Moreover, models can be generated by non-programmers, exported, and used on different devices, which allows for an interdisciplinary use.

Ultrastructural morphology of nontumorous lactotrophs in human pituitaries exposed to high prolactin levels

1987 ◽  
Vol 45 ◽  
pp. 896-897
Author(s):  
S. Jalalah ◽  
K. Kovacs ◽  
E. Horvath
Keyword(s):  
Anterior Pituitary ◽  
Secretory Activity ◽  
Pituitary Hormones ◽  
Feedback Mechanism ◽  
Endocrine Activity ◽  
Hormone Synthesis ◽  
Anterior Pituitary Hormones ◽  
Negative Feedback Mechanism ◽  
Ultrastructural Morphology ◽  
Transmission Electron

Lactotrophs, as many other endocrine cells, change their morphology in response to factors influencing their secretory activity. Secretion of prolactin (PRL) from lactotrophs, like that of other anterior pituitary hormones, is under the control of the hypothalamus. Unlike most anterior pituitary hormones, PRL has no apparent target gland which could modulate the endocrine activity of lactotrophs. It is generally agreed that PRL regulates its own release from lactotrophs via the short loop negative feedback mechanism exerted at the level of the hypothalamus or the pituitary. Accordingly, ultrastructural morphology of lactotrophs is not constant; it is changing in response to high PRL levels showing signs of suppressed hormone synthesis and secretion.By transmission electron microscopy and morphometry, we have studied the morphology of lactotrophs in nontumorous (NT) portions of 7 human pituitaries containing PRL-secreting adenoma; these lactotrophs were exposed to abnormally high PRL levels.

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Sign in / Sign up

Export Citation Format

Share Document