An automated detection and morphological classification of numerical abnormalities in human chromosomes

2013 ◽  
Author(s):  
A. Anu ◽  
R. Loganathan ◽  
M. Umadevi
Keyword(s):  
Automated Detection ◽  
Human Chromosomes ◽  
Morphological Classification

CLINICAL AND MORPHOLOGICAL CLASSIFICATION OF ACUTE PANCREATITIS AND ITS COMPLICATIONS

2016 ◽  
Author(s):  
S. N. Bogdanov ◽  
◽  
S. Ju. Babaev ◽  
A. V. Strazhnov ◽  
A. B. Stroganov ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Morphological Classification

Automated detection and classification of shoulder arthroplasty models using deep learning

2020 ◽  
Vol 49 (10) ◽  
pp. 1623-1632
Author(s):  
Paul H. Yi ◽  
Tae Kyung Kim ◽  
Jinchi Wei ◽  
Xinning Li ◽  
Gregory D. Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Shoulder Arthroplasty ◽  
Automated Detection

Automated detection and classification of spilled loads on freeways based on improved YOLO network

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Siqi Zhou ◽  
Yufeng Bi ◽  
Xu Wei ◽  
Jiachen Liu ◽  
Zixin Ye ◽  
...  
Keyword(s):  
Automated Detection

scholarly journals CNN architecture comparison for radio galaxy classification

2021 ◽  
Vol 503 (2) ◽  
pp. 1828-1846
Author(s):  
Burger Becker ◽  
Mattia Vaccari ◽  
Matthew Prescott ◽  
Trienko Grobler
Keyword(s):  
Comparative Study ◽  
Galaxy Evolution ◽  
Radio Galaxy ◽  
Recognition Performance ◽  
Model Complexity ◽  
Great Promise ◽  
Radio Sources ◽  
Morphological Classification ◽  
Computational Performance

ABSTRACT The morphological classification of radio sources is important to gain a full understanding of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citizen scientists, and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is convolutional neural networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. First, a proper analysis of whether overfitting occurs when training CNNs to perform radio galaxy morphological classification using a small curated training set is needed. Secondly, a good comparative study regarding the practical applicability of the CNN architectures in literature is required. Both of these shortcomings are addressed in this paper. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, computational complexity, and mean per class accuracy. As part of this study, we also investigate the effect that receptive field, stride length, and coverage have on recognition performance. For the sake of completeness, we also investigate the recognition performance gains that we can obtain by employing classification ensembles. A ranking system based upon recognition and computational performance is proposed. MCRGNet, Radio Galaxy Zoo, and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.

Morphological classification of crista fenestra of round window corridor during pediatric cochlear implantation

2021 ◽  
pp. 110816
Author(s):  
Saad Elzayat ◽  
Hitham H. Elfarargy ◽  
Islam Soltan ◽  
Mona A. Abdel-Kareem ◽  
Maurizio Barbara ◽  
...  
Keyword(s):  
Cochlear Implantation ◽  
Round Window ◽  
Morphological Classification

Assessment of Differentiation Aspects by the Morphological Classification of Embryoid Bodies Derived from Human Embryonic Stem Cells

2011 ◽  
Vol 20 (11) ◽  
pp. 1925-1935 ◽  
Author(s):  
Jung Mo Kim ◽  
Sung-Hwan Moon ◽  
Sung Geum Lee ◽  
Youn Jeong Cho ◽  
Ki Sung Hong ◽  
...  
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Embryonic Stem ◽  
Embryoid Bodies ◽  
Morphological Classification

Performance of the CellaVision DC-1 digital cell imaging analyser for differential counting and morphological classification of blood cell

2021 ◽  
pp. jclinpath-2021-207863
Author(s):  
Lisa N van der Vorm ◽  
Henriët A Hendriks ◽  
Simone M Smits
Keyword(s):  
Blood Cell ◽  
Peripheral Blood ◽  
Cell Imaging ◽  
Small Satellite ◽  
Morphological Classification ◽  
Clinical Sensitivity ◽  
Blood Smears ◽  
Low Volume ◽  
Peripheral Blood Smears

AimsRecently, a new automated digital cell imaging analyser (Sysmex CellaVision DC-1), intended for use in low-volume and small satellite laboratories, has become available. The purpose of this study was to compare the performance of the DC-1 with the Sysmex DI-60 system and the gold standard, manual microscopy.MethodsWhite blood cell (WBC) differential counts in 100 normal and 100 abnormal peripheral blood smears were compared between the DC-1, the DI-60 and manual microscopy to establish accuracy, within-run imprecision, clinical sensitivity and specificity. Moreover, the agreement between precharacterisation and postcharacterisation of red blood cell (RBC) morphological abnormalities was determined for the DC-1.ResultsWBC preclassification and postclassification results of the DC-1 showed good correlation compared with DI-60 results and manual microscopy. In addition, the within-run SD of the DC-1 was below 1 for all five major WBC classes, indicating good reproducibility. Clinical sensitivity and specificity were, respectively, 96.7%/95.9% compared with the DI-60% and 96.6%/95.3% compared with manual microscopy. The overall agreement on RBC morphology between the precharacterisation and postcharacterisation results ranged from 49% (poikilocytosis) to 100% (hypochromasia, microcytosis and macrocytosis).ConclusionsThe DC-1 has proven to be an accurate digital cell imaging system for differential counting and morphological classification of WBCs and RBCs in peripheral blood smears. It is a compact and easily operated instrument that can offer low-volume and small satellite laboratories the possibilities of readily available blood cell analysis that can be stored and retrieved for consultation with remote locations.

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