Keratoconus (KC) is a progressive eye disease, and it is the fourth leading cause of blindness worldwide. KC accounts for 200,000 irreversible blindness and low vision in the U.S. according to the National Institute of Health, National Eye Institute (NIH-NEI). In this paper, we propose a novel smartphone-based method for diagnosing keratoconus in its early stages using eye models. Our proposed method projects Placido discs utilizing the smartphone screen on the cornea and uses a novel color enhancement method for preprocessing, and CIE LAB color-based image processing technique to extract Placido discs from corneal images. The corneal topography map is calculated using Placido disc projections. Finally, by adopting the support vector machine (SVM) and k-fold cross-validation algorithm, we distinguished KC eyes from healthy eyes. From the 50 image database, our proposed algorithm distinguishes KC eyes from healthy eyes with 90% sensitivity 91% specificity and 95% accuracy. The proposed method provides an affordable, rapid, easy-to-use, and versatile method that could be used in remote areas with medical shortages for detecting KC by using smartphones without the use of bulky and expensive imaging devices.

COVID-19 has disrupted and irrevocably changed the everyday lives of people all around the world. This viral disease has created the necessity for a contact-free, non-invasive, and easy-to-use diagnostic device. In this paper, we propose a smartphone-based COVID-19 detection method that detects COVID-19 based on the coughing sound of patients. The proposed algorithm segments the coughing sounds collected from the raw audio signals acquired by a smartphone and then detects COVID-19 from the segmented coughing sounds. The proposed algorithm puts raw coughing sounds and the features extracted from the raw sounds into long-term short memory (LSTM), which is known to be effective in the regression and classification of periodic time series signals. Experimental results show that the proposed method applied to the Virufy dataset provides COVID-19 detection accuracy of 92% from the coughing segments. The proposed method has an advantage in pre-diagnosing COVID-19 since the proposed method only requires a smartphone Index Terms—COVID-19, LSTM., machine learning.

Eyesight is one of the most vital senses. In the year 2021, about 2.2 billion people will have vision impairment. Amongst them, 93 million were due to cataracts. In this paper, we have merged different image processing techniques and deep learning networks to diagnose and differentiate between various types of cataracts. The conventional Convolution Neural Network (CNN), in conjunction with support vector machines (SVM), classifies nuclear, cortical spoking, and capsular cataract eyes. The proposed method was successful in accurately classifying the two classes with an accuracy of 85.71%, while for a three-class classification, 83.33% was the maximum accuracy achieved.

Keratoconus is a progressive eye disease prevalent worldwide. Keratoconus is caused by the change in curvature of the eyeball. An unevenly shaped lens causes blurry and inaccurate vision in the patient, which eventually may cause blindness. The existing keratoconus detection techniques use a less accurate keratoscope and bulky, expensive topography imaging (OCT) device, which causes inconvenience in diagnosing this disease in a remote and scarce setup. In this paper, I propose a novel smartphone-based keratoconus diagnosis technique that uses a smartphone camera to acquire a 2D image of the cornea with Placido disc reflection, and process different image processing techniques including entropy-based edge detection, multiple circles detections, finally calculating 3D topography of the eye in an app. The existing inconvenient methods of keratoconus detection can be replaced by our proposed method, which is accurate, quick, reliable, and simple for usage by clinicians and patients in remote settings.