Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images

Alqudah, Ali Mohammad; Alquraan, Hiam; Qasmieh, Isam Abu; Al-Badarneh, Alaa

doi:10.4028/www.scientific.net/jbbbe.39.40

Cited by 28 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These measures indicate how precisely the x-ray chest images are diagnosed [26]. To compute these measures, four different types of statistical values are computed which are TP, FP, FN and TN [27,28]. Then using these values, the mentioned measurements have been computed as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021

Self Cite

View full text Add to dashboard Cite

BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classi cation of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates. ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose arti cial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a speci city of 96.68% for SVM and 96.60% for KNN. ConclusionsIn this study, an arti cial intelligence framework has been proposed for the detection and classi cation of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classi ers, namely, SVM and KNN; in addition to the SoftMax classi er which is the default CNN classi er. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

show abstract

Section: Performance Evaluationmentioning

confidence: 99%

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

2021

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Performance Evaluationmentioning

confidence: 99%

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Alqudah

Qazan

Masad

2021

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

show abstract

“…The SVM uses training data to create a model that distinguishes the data entered and that can be used to predict the new data class. The main objective of the SVM is to find the best hyperplane separating the entire dataset and optimizing the distance between the nearest data point and the hyperplane separating [51,52,53]. In this research, we have been used radial basis function (RBF).…”

Section: Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

Alqudah

Qazan

Alqudah

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

show abstract

Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images

Cited by 28 publications

References 32 publications

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

Contact Info

Product

Resources

About