Implementation of intelligent model for pneumonia detection

The advancement of technology in the field of artificial intelligence and neural networks allows us to improve speed and efficiency in the diagnosis of various types of problems. In the last few years, the rise in the field of convolutional neural networks has been particularly noticeable, showing promising results in problems related to image processing and computer vision. Given that humans have limited ability to detect patterns in individual images, accurate diagnosis can be a problem for even medical professionals. In order to minimize the number of errors and unintended consequences, computer programs based on neural networks and deep learning principles are increasingly used as assistant tools in medicine. The aim of this study was to develop a model of an intelligent system that receives x-ray image of the lungs as an input parameter and, based on the processed image, returns the possibility of pneumonia as an output. The implementation of this functionality was implemented through transfer learning methodology based on already defined convolution neural network architectures.

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A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit19525 ◽

2019 ◽

pp. 102-112

Author(s):

J. Juditha Mercina ◽

J. Madhumathi ◽

V. Priyanga ◽

M. Deva Priya

Keyword(s):

Neural Network ◽

Neural Networks ◽

Lung Disease ◽

Lung Diseases ◽

Intelligent System ◽

Lung Disorder ◽

X Ray ◽

Cad System ◽

Computer Aided ◽

Aided Diagnosis

Lungs play an important role in human respiratory system. There are diseases that affect the functioning of lungs. To analyse lung diseases in the chest region using X-ray based Computer-Aided Diagnosis (CAD) system, it is necessary to determine the lung regions subject to analysis. In this paper, an intelligent system is proposed for lung disease detection. In this paper, Interstitial Lung Disease (ILD) patterns are classified using Convolutional Neural Networks (CNN). The proposed system involves five convolutional layers and three dense layers. The performance of the classification demonstrates the potential of CNN in analysing lung patterns.

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Accurate Diagnosis of COVID-19 Based on Deep Neural Networks and Chest X-Ray Images. (Dept.E (Electronics ))

Bulletin of the Faculty of Engineering. Mansoura University ◽

10.21608/bfemu.2020.104387 ◽

2020 ◽

Vol 45 (3) ◽

pp. 11-15

Author(s):

Hossam El-Din Moustafa ◽

Mervat El-Seddek

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Accurate Diagnosis ◽

X Ray ◽

Chest X Ray

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Accurate Diagnosis of COVID-19 Based on Deep Neural Networks and Chest X-Ray Images

Bulletin of the Faculty of Engineering. Mansoura University ◽

10.21608/bfemu.2020.30471.1001 ◽

2020 ◽

Vol 0 (0) ◽

pp. 0-0

Author(s):

Hossam El-Din Moustafa ◽

Mervat El-Seddek

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Accurate Diagnosis ◽

X Ray ◽

Chest X Ray

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Deep Convolutional Neural Networks for Accurate Diagnosis of COVID-19 Patients Using Chest X-Ray Image Databases from Italy, Canada, and the USA

Journal of Respiratory Infections ◽

10.18297/jri/vol5/iss1/34 ◽

2021 ◽

Vol 5 (1) ◽

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Image Databases ◽

Accurate Diagnosis ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

The Usa ◽

Chest X Ray

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Pneumonia and Eye Disease Detection using Convolutional Neural Networks

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3503 ◽

2020 ◽

Vol 10 (3) ◽

pp. 5769-5774 ◽

Cited By ~ 1

Author(s):

P. Chakraborty ◽

C. Tharini

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Detection System ◽

Eye Disease ◽

Medical Professionals ◽

Disease Detection ◽

Training Time ◽

X Ray ◽

Detection Systems

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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Identifying Skeletal Maturity from X-rays using Deep Neural Networks

The Open Biomedical Engineering Journal ◽

10.2174/1874120702115010141 ◽

2021 ◽

Vol 15 (1) ◽

pp. 141-148

Author(s):

Suprava Patnaik ◽

Sourodip Ghosh ◽

Richik Ghosh ◽

Shreya Sahay

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Neural Network ◽

Deep Neural Networks ◽

Skeletal Maturity ◽

Bone Age ◽

Network Architectures ◽

X Rays ◽

X Ray ◽

Regression Techniques

Skeletal maturity estimation is routinely evaluated by pediatrics and radiologists to assess growth and hormonal disorders. Methods integrated with regression techniques are incompatible with low-resolution digital samples and generate bias, when the evaluation protocols are implemented for feature assessment on coarse X-Ray hand images. This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. Further, in the same order, the MAE accomplished by the two models are 12.583 and 13.299 respectively, when subjected to very few training instances with negligible chances of overfitting.

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Color Space Transformation using Neural Networks

Color and Imaging Conference ◽

10.2352/issn.2169-2629.2019.27.29 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 153-158

Author(s):

Lindsay MacDonald

Keyword(s):

Neural Network ◽

Neural Networks ◽

Color Space ◽

Reflectance Spectra ◽

Network Architectures ◽

Color Spaces ◽

Natural Materials ◽

Space Transformation ◽

Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

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