scholarly journals Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

2021 ◽  
Vol 12 (4) ◽  
pp. 01-27
Author(s):  
Sezin Barin ◽  
Murat Saribaş ◽  
Beyza Gülizar Çiltaş ◽  
Gür Emre Güraksin ◽  
Utku Köse
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Intracranial Hemorrhage ◽  
Hybrid Method ◽  
Computer Aided Design ◽  
Cranial Computed Tomography ◽  
Critical Problem ◽  
Computed Tomography Scans ◽  
Aided Design

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.

scholarly journals Deep Learning Applied to Intracranial Hemorrhage Detection

2021 ◽  
Author(s):  
Luis Cortes-Ferre ◽  
Miguel Angel Gutiérrez-Naranjo ◽  
Juan José Egea-Guerrero ◽  
Marcin Balcerzyk
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Intracranial Hemorrhage ◽  
The Other ◽  
Patient Diagnosis ◽  
Learning Techniques ◽  
Computed Tomography Scans ◽  
Novel Method ◽  
The One ◽  
Urgent Procedure

Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper, we propose a novel method based on Deep Learning techniques which can be useful as decision support system. Our proposal is two-folded. On the one hand, the proposed technique classifies slices of computed tomography scans for hemorrhage existence or not, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our method provides visual explanation to the chosen classification by using the so-called Grad-CAM method. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

scholarly journals Deep Learning based Staging of Bone Lesions from Computed Tomography Scans (January 2021)

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Samira Masoudi ◽  
Sherif Mehralivand ◽  
Stephanie A. Harmon ◽  
Nathan Lay ◽  
Liza Lindenberg ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Bone Lesions ◽  
Computed Tomography Scans

Making Data Big for a Deep-learning Analysis: Aggregation of Public COVID-19 Datasets of Lung Computed Tomography Scans

2021 ◽  
Author(s):  
Francesca Lizzi ◽  
Francesca Brero ◽  
Raffaella Cabini ◽  
Maria Fantacci ◽  
Stefano Piffer ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Computed Tomography Scans ◽  
Learning Analysis

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Cranial computed tomography scans of premature babies predict their eventual learning disabilities

1997 ◽  
Vol 16 (4) ◽  
pp. 319-322 ◽  
Author(s):  
Akira Ishida ◽  
Wako Nakajima ◽  
Hirokazu Arai ◽  
Yasushi Takahashi ◽  
Riho Iijima ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Learning Disabilities ◽  
Cranial Computed Tomography ◽  
Premature Babies ◽  
Computed Tomography Scans

Rapid Detection of Neonatal Intracranial Hemorrhage by Transillumination

PEDIATRICS ◽  
1979 ◽  
Vol 64 (6) ◽  
pp. 843-847
Author(s):  
Steven M. Donn ◽  
Marcia J. Sharp ◽  
Lawrence R. Kuhns ◽  
Joaquin O. Uy ◽  
James E. Knake ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cerebrospinal Fluid ◽  
Light Beam ◽  
Intracranial Hemorrhage ◽  
Rapid Detection ◽  
Light Transmission ◽  
Postmortem Examination ◽  
Cranial Computed Tomography ◽  
Anterior Fontanel ◽  
Light Passing

Using transillumination and a sensitive cadmium sulfide light meter, 145 newborns were screened for the presence of intracranial hemorrhage. Intracranial hemorrhage (ICH) was suspected when the light meter could not detect any light passing through the anterior fontanel when the light beam was directed through the frontal eminence. ICH was confirmed by cranial computed tomography or postmortem examination in all 17 infants not transmitting light. Spectrophotometry was performed on samples of cerebrospinal fluid (CSF) to demonstrate the mechanism through which blood in the CSF blocks light transmission.

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