A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms

Abstract

A method to extract the retina characteristic points for the purpose of medical diagnosis of the human eye is presented in this research. The proposed method helps to make the primary decision about the illness faster and can be used on mobile devices. The algorithm is mostly based on the characteristic points (the so-called minutiae). These structures are commonly used in the biometric applications for fingerprintbased people recognition. In the case of the conducted research, this trait was used to differentiate healthy eyes from unhealthy ones. The methods were evaluated by appropriately implemented algorithms, showing promising results. Each solution was created with object-oriented programming language. The accuracy of the classification (healthy versus samples with pathological changes) was evaluated using four algorithms: k-Nearest Neighbors, k-Means and Support Vector Machines (SVM) with linear and third-degree polynomial as well as our own approach based on counting the minutiae number. Performance requirements were also checked, and it was verified that the computing power of modern mobile devices is sufficient to implement the proposed solution. The highest accuracy result was equal to 96,45% and was obtained with the third-degree polynomial SVM. This was a novel approach. For comparative purposes, we also implemented currently used solutions for image analysis-deep learning (DL) and Convolution Neural Networks (CNNs). Both medical and computer science backgrounds are presented in the work with the main methodology components to include image segmentation using the Gaussian Matched Filter, binarization by Local Entropy Thresholding and classification with the previously mentioned approaches.

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

“…This algorithm was also selected due to its high efficiency. Moreover, the authors also proven its usefulness in their previous research [25]. During experiments, the authors observed that linear classification is much more accurate than nonlinear.…”

Purchase behavior in COVID-19: A cross study in Mexico, Colombia, and Ecuador

“…The time, space, and place in the consumption process is visible now in the purchasing behavior with the social distancing, the healthy distance, and the commercial restriction caused by the Covid-19 health contingency. Despite being, above all, health -and also a humanitarian-crisis, the Covid-19 pandemic presents severe economic consequences throughout the world ( Cox, 2020 ), as an increase in unemployment rates ( Hall, 2020 ; Rushe and Aratani, 2020 ) collapsed health systems ( Washingtonpost, 2020 ), education models overwhelmed by technology ( worldbank, 2020 ), supply chains disrupted by border closings ( Buchholz, 2020 ; Salcedo et al., 2020 ), international and domestic tourism suspended due to a lack of sanitary protocols ( OECD, 2020 ), social coexistence curtailed by significantly increased infections ( Dubey et al., 2020 ) and a decreasing demand by consumers for the closure of companies ( Szymkowski et al., 2020 ), mainly in small and medium-sized companies (SMEs). These factors are also part of the purchasing behavior that is changing ( Dooley et al., 2010 ; Foxall, 1979 ; Lamming, 2000 ; Habel et al., 2020 ).…”

Pre-processing methods in chest X-ray image classification

“…At the same time, modern technologies have gathered more interest. Artificial intelligence (AI) and ML can be used in numerous applications such as cybersecurity [ 2 ], pedestrian detection [ 3 ], telemedicine [ 4 ], biometrics [ 5 ] or sports analytics [ 6 ]. Thus, the implementation of AI and ML in COVID-19 and other lung diseases seems to be the desired natural progression.…”