scholarly journals A few notes about obstetric forceps

2020 ◽  
Vol 9 (1) ◽  
pp. 25-30
Author(s):  
E. F. Cherneviy
Keyword(s):  
Medical Instrument ◽  
Obstetric Forceps

It is known that in order to judge the merits or demerits of one or another medical instrument, it is necessary to accurately formulate the requirements that are imposed on it and then decide how much it satisfies these requirements.

Obstetric Forceps

BMJ ◽  
1965 ◽  
Vol 1 (5430) ◽  
pp. 305-305
Author(s):  
M. P. Embrey
Keyword(s):  
Obstetric Forceps

scholarly journals Torsional waveguide modeling of an ultrasonic medical instrument

2021 ◽  
Vol 1902 (1) ◽  
pp. 012009
Author(s):  
Alexander Mikhailovich Gouskov ◽  
Yuri Vsevolodovich Grigoryev ◽  
Phyo Aung Pyae
Keyword(s):  
Medical Instrument

scholarly journals The Use of the Obstetric Forceps

1882 ◽  
Vol 107 (12) ◽  
pp. 266-270
Author(s):  
J. H. MCCOLLOM
Keyword(s):  
Obstetric Forceps

Adverse drug and medical instrument reactions in a pediatric intensive care unit

2009 ◽  
Vol 18 (8) ◽  
pp. 761-762 ◽  
Author(s):  
Shockrollah Farrokhi ◽  
Zahra Pourpak ◽  
Fatemeh Fattahi ◽  
Mahmood Reza Ashrafi ◽  
Parvin Majdinasab ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Medical Instrument ◽  
Pediatric Intensive Care

The Use of Obstetric Forceps

1964 ◽  
Vol 35 (6) ◽  
pp. 606-610
Author(s):  
Benjamin Tenney
Keyword(s):  
Obstetric Forceps

scholarly journals AN INTELLIGENT CBMIR SYSTEM FOR DETECTION AND LOCALIZATION OF LUNG DISEASES

2021 ◽  
Vol 9 (08) ◽  
pp. 651-660
Author(s):  
Nora I. Yahia ◽  
◽  
Ayman I. Al-Dosouki ◽  
Sahar A. Mokhtar ◽  
Hany M. Harb ◽  
...  
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Medical Instrument ◽  
Detection Accuracy ◽  
Retrieval Process ◽  
Recognition Efficiency ◽  
Imaging Signs ◽  
Detection And Localization ◽  
Aided Diagnosis ◽  
Detector Model

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

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