Un-Supervised MRI Segmentation Using Self Organised Maps

Prajapati, Praveen Kumar; Dixit, Manish

doi:10.1109/cicn.2015.97

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…Use of these imaging techniques enhance the accuracy of decisions to be reached by radiographers when determining the presence or absence of irregularities in medical images which are under analysis for correct diagnosis [39,40]. MRI is non-invasive and known for providing detailed information for tissue identification [40,41]. Unlike supervised segmentation, unsupervised segmentation of MRI images is automatic or semi-automatic.…”

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

“…It does not require prior knowledge or intervention of an expert. Hence, it consumes less time and effort [41].…”

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

“…Finding ways for significant reduction in time requirement for FCM was recommended [45]. In [41], an unsupervised algorithm using self-organising maps for segmentation of MRI images was proposed. SOM classifier was used for brain tissue classification, following pre-processing of acquired MR brain images, feature extraction and feature selection with principal component analysis.…”

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

“…The proposed technique achieved good segmentation results. Besides, the use of principal component analysis was helpful in achieving optimum results and reducing the time required for segmentation [41].…”

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

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A Review of Unsupervised Artificial Neural Networks with Applications

Fabiyi¹

2019

IJCA

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Artificial Neural Networks (ANNs) are models formulated to mimic the learning capability of human brains. Learning in ANNs can be categorized into supervised, reinforcement and unsupervised learning. Application of supervised ANNs is limited to when the supervisor's knowledge of the environment is sufficient to supply the networks with labelled datasets. Application of unsupervised ANNs becomes imperative in situations where it is very difficult to get labelled datasets. This paper presents the various methods, and applications of unsupervised ANNs. In order to achieve this, several secondary sources of information, including academic journals and conference proceedings, were selected. Autoencoders, self-organizing maps, and boltzmann machines are some of the unsupervised ANNs based algorithms identified. Some of the areas of application of unsupervised ANNs identified include exploratory data, statistical, biomedical, industrial, financial and control analysis. Unsupervised algorithms have become very useful tools in segmentation of Magnetic resonance images for detection of anomalies in the body systems.

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Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

“…It does not require prior knowledge or intervention of an expert. Hence, it consumes less time and effort [41].…”

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

Section: Unsupervised Neural Network In Segmentation Of Magnetic Resmentioning

confidence: 99%

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A Review of Unsupervised Artificial Neural Networks with Applications

Fabiyi¹

2019

IJCA

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“…Thus, SNORT is unable to detect new types of attacks, typically called 'zero day' attacks [4]. Deep learning systems are promising candidates for the improvement of anomaly-based intrusion detection systems due to their pattern recognition and pattern reconstruction capabilities [5,6]. Deep networks that are adaptable and can perform self-organization to learn new features [4] are needed to detect 'zero day' attacks in real-time.…”

Section: Introductionmentioning

confidence: 99%

Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection

Alam

Fernando

Jaoudi

et al. 2019

Proceedings of the International Conference on Neuromorphic Systems

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Custom low power hardware for real-time network security and anomaly detection are in great demand, as these would allow for efficient security in battery-powered network devices. This paper presents a memristor based system for real-time intrusion detection, as well as an anomaly detection based on autoencoders. Intrusion detection is based on a single autoencoder, and the overall detection accuracy of this system is 92.91% with a malicious packet detection accuracy of 98.89%. The system described in this paper is also capable of using two autoencoders to perform anomaly detection using real-time online learning. Using this system, we show that anomalous data is flagged by the system, but over time the system stops flagging a particular datatype if its presence is abundant. Utilizing memristors in these designs allows us to present extreme low power systems for intrusion and anomaly detection, while sacrificing little accuracy. CCS CONCEPTSCCS → Hardware → Emerging technologies → Analysis and design of emerging devices and systems → Emerging architectures

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A CAD system design using iteratively reweighted fuzzy c-means and deep tree training for Alzheimer's disease diagnosis

Sasikala

2024

Biomedical Signal Processing and Control

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Un-Supervised MRI Segmentation Using Self Organised Maps

Cited by 7 publications

References 9 publications

A Review of Unsupervised Artificial Neural Networks with Applications

A Review of Unsupervised Artificial Neural Networks with Applications

Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection

A CAD system design using iteratively reweighted fuzzy c-means and deep tree training for Alzheimer's disease diagnosis

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