Amin Askarian
Research Subjects
Amin Askarian has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Behnam Askarian
College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
1 shared publication - Fatemehsadat Tabei
Department of Electrical Engineering/Computer Science, West Texas A & M University, Canyon, TX 79016, USA
1 shared publication - Jo Woon Chong
School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
1 shared publication
Latest Publications
- Open Access
An IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology
Keratoconus (KC) is a progressive eye disease and a major cause of vision impairment and blindness worldwide. Early diagnosis is crucial for effective management, yet conventional diagnostic methods rely on expensive and bulky imaging devices, limiting accessibility, especially in resource-constrained settings. This paper introduces a novel smartphone-based approach for the early detection of KC, leveraging screen-projected Placido disc patterns and an advanced image processing framework. Unlike traditional corneal topographers, our method utilizes a unique Placido disc projection technique and a machine learning-based classification model to analyze corneal irregularities with high precision. With a sensitivity of 96.08%, specificity of 97.96%, and overall accuracy of 97% on our dataset, the proposed system demonstrates exceptional diagnostic reliability. By transforming a standard smartphone into an effective screening tool, this innovation provides an affordable, portable, and user-friendly solution for early KC detection, bridging the gap in eye care accessibility and reducing the global burden of undiagnosed keratoconus.
Open AccessAn IoT-Enabled mHealth Sensing Approach for Remote Detection of Keratoconus Using Smartphone Technology
Keratoconus (KC) is a progressive eye disease and a major cause of vision impairment and blindness worldwide. Early diagnosis is crucial for effective management, yet conventional diagnostic methods rely on expensive and bulky imaging devices, limiting accessibility, especially in resource-constrained settings. This paper introduces a novel smartphone-based approach for the early detection of KC, leveraging screen-projected Placido disc patterns and an advanced image processing framework. Unlike traditional corneal topographers, our method utilizes a unique Placido disc projection technique and a machine learning-based classification model to analyze corneal irregularities with high precision. With a sensitivity of 96.08%, specificity of 97.96%, and overall accuracy of 97% on our dataset, the proposed system demonstrates exceptional diagnostic reliability. By transforming a standard smartphone into an effective screening tool, this innovation provides an affordable, portable, and user-friendly solution for early KC detection, bridging the gap in eye care accessibility and reducing the global burden of undiagnosed keratoconus.