Osteoarthritis Disease Detection System using Self Organizing Maps Method based on Ossa Manus X-Ray

Automatic disease detection systems based on Convolutional Neural Networks (CNNs) are proposed in this paper for helping the medical professionals in the detection of diseases from scan and X-ray images. CNN based classification helps decision making in a prompt manner with high precision. CNNs are a subset of deep learning which is a branch of Artificial Intelligence. The main advantage of CNNs compared to other deep learning algorithms is that they require minimal pre-processing. In the proposed disease detection system, two medical image datasets consisting of Optical Coherence Tomography (OCT) and chest X-ray images of 1-5 year-old children are considered and used as inputs. The medical images are processed and classified using CNN and various performance measuring parameters such as accuracy, loss, and training time are measured. The system is then implemented in hardware, where the testing is done using the trained models. The result shows that the validation accuracy obtained in the case of the eye dataset is around 90% whereas in the case of lung dataset it is around 63%. The proposed system aims to help medical professionals to provide a diagnosis with better accuracy thus helping in reducing infant mortality due to pneumonia and allowing finding the severity of eye disease at an earlier stage.

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Intrusion Detection System using Self Organizing Maps

2009 International Conference on Intelligent Agent & Multi-Agent Systems ◽

10.1109/iama.2009.5228074 ◽

2009 ◽

Cited By ~ 18

Author(s):

V. K. Pachghare ◽

Parag Kulkarni ◽

Deven M. Nikam

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Self Organizing Maps ◽

Self Organizing

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A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

10.1101/2020.06.18.20134718 ◽

2020 ◽

Cited By ~ 2

Author(s):

Md. Zabirul Islam ◽

Md. Milon Islam ◽

Amanullah Asraf

Keyword(s):

Short Term Memory ◽

Detection System ◽

Medical Science ◽

Disease Detection ◽

X Ray ◽

Deep Feature ◽

Deep Cnn ◽

Lstm Network ◽

Deep Feature Extraction ◽

Novel Coronavirus

AbstractNowadays automatic disease detection has become a crucial issue in medical science with the rapid growth of population. Coronavirus (COVID-19) has become one of the most severe and acute diseases in very recent times that has been spread globally. Automatic disease detection framework assists the doctors in the diagnosis of disease and provides exact, consistent, and fast reply as well as reduces the death rate. Therefore, an automated detection system should be implemented as the fastest way of diagnostic option to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 421 X-ray images including 141 images of COVID-19 is used as a dataset in this system. The experimental results show that our proposed system has achieved 97% accuracy, 91% specificity, and 100% sensitivity. The system achieved desired results on a small dataset which can be further improved when more COVID-19 images become available. The proposed system can assist doctors to diagnose and treatment the COVID-19 patients easily.

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X-Ray Fluorescence Analysis and Self-Organizing Maps Classification of the Etruscan Gold Coin Collection at the Monetiere of Florence

Applied Spectroscopy ◽

10.1177/0003702816641421 ◽

2016 ◽

Vol 71 (5) ◽

pp. 817-822 ◽

Cited By ~ 3

Author(s):

Claudio Arias ◽

Stefano Bani ◽

Fiorenzo Catalli ◽

Giulia Lorenzetti ◽

Emanuela Grifoni ◽

...

Keyword(s):

Fluorescence Analysis ◽

Gold Alloys ◽

Gold Coin ◽

Self Organizing Maps ◽

X Ray ◽

Fundamental Parameters ◽

The World ◽

Self Organizing

The “Monetiere” of Florence hosts the most important collection of Etruscan coins in the world. In the framework of the longstanding collaboration between the Monetiere and the Applied Laser and Spectroscopy Laboratory in Pisa, the Etruscan gold coin collection of the museum was studied. The measurements were performed at the Monetiere, using a portable energy-dispersive X-ray fluorescence (XRF) instrument. The quantitative determination of the gold alloys used for the realization of the coins was obtained applying the fundamental parameters method to the XRF spectra; as a result, using the self-organizing maps method, we were able to classify the coins in four main groups. The main parameter determining the classification is the quantity of silver in the alloy. The results obtained shed some light about the origin of the coins under study.

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