A Review of Iris Recognition System ROI and Accuracy

Iris contains some information about human body and organ condition. Iridology is a scientific study of the iris structure to get some information that represents the condition of the various organs by examining the tissue strengths and weaknesses in the iris. With the rapid development of image processing, iridology has become more popular and reliable. In recent years, many systems that adopt iridology have been developed to diagnose a disease by analyzing a certain part of the iris or so-called Region of Interest (ROI). Typically, iris recognition systems consist of three main functions namely image pre-processing, feature extraction, and classification. This paper shows all the regions that have been studied and the accuracy of their iris recognition syste

Classification of Cornel Arcus using Texture Features with Bayesian Regulation Back Propagation

The corneal arcus (CA) is an eye problem frequently faced by some group of people. The CA signs indicate the presence of abnormal lipid in blood and can cause  several problems such as  blood pressure, diabetes, and hyperlipidemia. This paper presents a comparison of classification of the abnormal eye using a neural network. In order to extract the image features,  the gray level co-occurrence matrix (GLCM)was used. This matrix measures the texture of the image, where the statistical calculation can be used to present the image features. The Bayesian Regulation (BR) algorithm has been proposed, in which this classifier classifies the obtained results better than previous works by other researchers. In this experiment, two classes data-set of the eye image, normal and abnormal images CA are used. The results from this BR classifier demonstrate a sensitivity of 96.1 % and a specificity of 98.6 %. The overall accuracy of this proposed system is 97.6 %. Although this classifier does not obtain 100 % accuracy, however its result is  proven to be able to classify the CA images successfully

Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AI-based non-invasive and secured diagnosis for OLD using physiological and iris features. This research work implements different machine-learning-based techniques which classify various subjects, which are healthy and effective patients. The iris features include gray-level run-length matrix-based features, gray-level co-occurrence matrix, and statistical features. These features are extracted from iris images. Additionally, ten different classifiers and voting techniques, including hard and soft voting, are implemented and tested, and their performances are evaluated using several parameters, which are precision, accuracy, specificity, F-score, and sensitivity. Based on the statistical analysis, it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%. Keywords: Obstructive lung disease; non-invasive diagnosis; machine learning; physiological features; voting technique

A deep learning approach for kidney disease recognition and prediction through image processing

Chronic kidney disease (CKD) is a gradual decline in renal function that can lead to kidney damage or failure. As the disease progresses, it becomes harder to diagnose. Using routine doctor consultation data to evaluate various stages of CKD could aid in early detection and prompt intervention. To this end, researchers propose a strategy for categorizing CKD using an optimization technique inspired by the learning process. Artificial intelligence has the potential to make many things in the world seem possible, even causing surprise with its capabilities. Some doctors are looking forward to advancements in technology that can scan a patient’s body and analyse their diseases. In this regard, advanced machine learning algorithms have been developed to detect the presence of kidney disease. This research presents a novel deep learning model, which combines a fuzzy deep neural network, for the recognition and prediction of kidney disease. The results show that the proposed model has an accuracy of 99.23%, which is better than existing methods. Furthermore, the accuracy of detecting chronic disease can be confirmed without doctor involvement as future work. Compared to existing information mining classifications, the proposed approach shows improved accuracy in classification, precision, F-measure, and sensitivity metrics.Web of Science136art. no. 362