Identification of normal and abnormal brain hemorrhage on magnetic resonance images

Kakhandaki, Nita; Kulkarni, S.B.

doi:10.1016/b978-0-12-819443-0.00005-2

Cited by 4 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CAD systems can play an important role in early diagnosis of haemorrhage. CAD offers quick and consistent results with improved accuracy, thus enabling clinicians to make better treatment planning and strategic decisions [15,16]. The triage workflow can also be streamlined and optimized by automating the routine scanning of CT images with little or no human intervention.…”

Section: Computer Aided Diagnosis (Cad) For Haemorrhage Detectionmentioning

confidence: 99%

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Raghavendra

Pham

Gudigar

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Brain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.

show abstract

Section: Computer Aided Diagnosis (Cad) For Haemorrhage Detectionmentioning

confidence: 99%

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Raghavendra

Pham

Gudigar

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…CAD systems offer more reliable, reproducible, and accurate clinical features, which can aid clinicians in appropriate treatment, planning, and strategic decision-making [13,14]. Computer-assisted techniques can significantly reduce human error and enable quick and cost-effective detection and evaluation of hematoma.…”

Section: Related Workmentioning

confidence: 99%

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

et al. 2022

View full text Add to dashboard Cite

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

show abstract

Detection of Cancer and Comparison of Survival Rate of Patients from CT Images on Long Nodules Data using Novel Support Vector Machine and 3D Convolutional Neural Network Classification

Babu

2022

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)

View full text Add to dashboard Cite

Identification of normal and abnormal brain hemorrhage on magnetic resonance images

Cited by 4 publications

References 15 publications

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images

Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Detection of Cancer and Comparison of Survival Rate of Patients from CT Images on Long Nodules Data using Novel Support Vector Machine and 3D Convolutional Neural Network Classification

Contact Info

Product

Resources

About