Proceedings ArticleDOI
A Survey on Deep Learning for Intracranial Hemorrhage Detection
Harshali Rane,Krishna K. Warhade +1 more
- pp 38-42
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TLDR
In this article, the authors provide a survey of deep learning approaches for automatic detection of acute intracranial hemorrhage and its subtypes and provide key evaluation metrics and loss functions addressing the data imbalance underlying with the problem statement.Abstract:
Computed Tomography (CT) images of the patient’s cranium are widely used in the diagnosis of hemorrhage. They are reviewed by highly skilled professionals to find the existence, location and type of hemorrhage. This process is complex, labor-intensive and requires lot of time. The massive progress in machine learning techniques and availability of high computational power has made it possible to detect such hemorrhages automatically. The paper provides survey of recent deep learning approaches for automatic detection of such acute intracranial hemorrhage and its subtypes. Further the experimental results using windowing technique inspired by practical radiologist’s approach and relevant research papers is provided to verify its efficacy as a pre-processing step before feeding DICOM images to the network. Transfer learning approach is used to detect hemorrhage types. The open source Kaggle platform is utilized for computational resources and for carrying out the experimental study. Some key evaluation metrics and loss functions addressing the data imbalance underlying with the problem statement are also presented. The paper concludes with addressing key insights into open issues in the proposed domain.read more
Citations
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Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study
Animesh Kumar Paul,Anushree Bose,Sunil V. Kalmady,Venkataram Shivakumar,Vanteemar S. Sreeraj,Rujuta Parlikar,Janardhanan C. Narayanaswamy,Serdar M. Dursun,Andrew J. Greenshaw,Russell Greiner,Ganesan Venkatasubramanian +10 more
TL;DR: First evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy is reported.
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Intracerebral Hemorrhage Detection in Computed Tomography Scans Through Cost-Sensitive Machine Learning
TL;DR: In this paper , 6 machine learning models were trained on 160 computed tomography brain scans both with and without utility matrices based on penalization, an implementation of cost-sensitive learning.
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Intracerebral Hemorrhage Detection in Computed Tomography Scans Through Cost-Sensitive Machine Learning
TL;DR: In this article, 6 machine learning models were trained on 160 computed tomography brain scans both with and without utility matrices based on penalization, an implementation of cost-sensitive learning.
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Automated Intracranial Haemorrhage Detection and Classification using Rider Optimization with Deep Learning Model
TL;DR: In this article , the authors developed an automated ICH detection and classification using Rider Optimization with Deep Learning (ICHDC-RODL) model using Xtended Central Symmetric Local Binary Pattern (XCS-LBP) model.
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Automated Intracranial Haemorrhage Detection and Classification using Rider Optimization with Deep Learning Model
T. Karthik,Naziya Hussain,N. K. Anushkannan,Rajasekhar Pinnamaneni,Vijayakrishna Rapaka E,Shyamali Das +5 more
TL;DR: In this paper , the authors developed an automated ICH detection and classification using Rider Optimization with Deep Learning (ICHDC-RODL) model using Xtended Central Symmetric Local Binary Pattern (XCS-LBP) model.
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Proceedings Article
Window Classification of Brain CT Images in Biomedical Articles
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