A fast and adaptive automated disease diagnosis method with an innovative neural network model

Alkım, Erdem; Gürbüz, Emre; Kılıç, Erdal

doi:10.1016/j.neunet.2012.04.010

Cited by 37 publications

(20 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every element (i.e., neuron) on the hidden layer represents a reference vector. Input vector is mapped into a reference vector which consists of the weights between the input layer and the hidden layer (Alkim, Gurbuz, & Kilic, 2012).…”

Section: Methodsmentioning

confidence: 99%

Migraine diagnosis by using artificial neural networks and decision tree techniques

Çelik¹,

Yurtay²,

Yilmaz³

2014

AJIT-e

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Migraine diagnosis by using artificial neural networks and decision tree techniques

Çelik¹,

Yurtay²,

Yilmaz³

2014

AJIT-e

View full text Add to dashboard Cite

show abstract

“…Based on these, ANNs offer high prediction capabilities. ANNs also have been successfully applied in various real life scenarios which include learning systems [13], neuroscience [14], and engineering [15]. Through these, it is believed that ANNs are a powerful tool which has the ability to make predictions based on complex relations of the input and output data.…”

Section: Ann and Prediction Related Workmentioning

confidence: 99%

A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

Savva

Theocharides

Nicopoulos

2017

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN) for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96-99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle.

show abstract

“…In recent times ANNs are becoming quite popular in medicine, particularly for clinical diagnosis based on experimental and clinical data [9][10][11][12][13]. ANNs are a convenient tool in various tasks such as for blood cell classification, EEG, EMG, ECG analysis and bone fracture healing assessment, for diagnosis, healing, and prognosis in hypertension [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks approach to early lung cancer detection

Goryński

Safian

Grądzki³

et al. 2014

Open Medicine

View full text Add to dashboard Cite

AbstractLung cancer is rated with the highest incidence and mortality every year compared with other forms of cancer, therefore early detection and diagnosis is essential. Artificial Neural Networks (ANNs) are “artificial intelligence” software which have been used to assess a few prognostic situations. In this study, a database containing 193 patients from Diagnostic and Monitoring of Tuberculosis and Illness of Lungs Ward in Kuyavia and Pomerania Centre of the Pulmonology (Bydgoszcz, Poland) was analysed using ANNs. Each patient was described using 48 factors (i.e. age, sex, data of patient history, results from medical examinations etc.) and, as an output value, the expected presence of lung cancer was established. All 48 features were retrospectively collected and the database was divided into a training set (n=97), testing set (n=48) and a validating set (n=48). The best prediction score of the ANN model (MLP 48-9-2) was above 0.99 of the area under a receiver operator characteristic (ROC) curve. The ANNs were able to correctly classify 47 out of 48 test cases. These data suggest that Artificial Neural Networks can be used in prognosis of lung cancer and could help the physician in diagnosis of patients with the suspicion of lung cancer.

show abstract

A fast and adaptive automated disease diagnosis method with an innovative neural network model

Cited by 37 publications

References 35 publications

Migraine diagnosis by using artificial neural networks and decision tree techniques

Migraine diagnosis by using artificial neural networks and decision tree techniques

A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems

Artificial neural networks approach to early lung cancer detection

Contact Info

Product

Resources

About