1
|
Batool S, Gilani SO, Waris A, Iqbal KF, Khan NB, Khan MI, Eldin SM, Awwad FA. Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images. Sci Rep 2023; 13:14462. [PMID: 37660096 PMCID: PMC10475020 DOI: 10.1038/s41598-023-41797-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 08/31/2023] [Indexed: 09/04/2023] Open
Abstract
Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.
Collapse
Affiliation(s)
- Summiya Batool
- National University of Sciences and Technology, Islamabad, 44000, Pakistan
| | - Syed Omer Gilani
- National University of Sciences and Technology, Islamabad, 44000, Pakistan
| | - Asim Waris
- National University of Sciences and Technology, Islamabad, 44000, Pakistan
| | | | - Niaz B Khan
- National University of Sciences and Technology, Islamabad, 44000, Pakistan
- Mechanical Engineering Department, College of Engineering, University of Bahrain, Isa Town, 32038, Bahrain
| | - M Ijaz Khan
- Depaetment of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, Lebanon.
- Department of Mathematics and Statistics, Riphah International University I-14, Islamabad, 44000, Pakistan.
- Department of Mechanics and Engineering Science, Peking University, Beijing, 100871, China.
| | - Sayed M Eldin
- Faculty of Engineering, Center of Research, Future University in Egypt, New Cairo, 11835, Egypt
| | - Fuad A Awwad
- Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, 11587, Riyadh, Saudi Arabia
| |
Collapse
|
2
|
Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition. J Pers Med 2021; 11:jpm11111172. [PMID: 34834524 PMCID: PMC8621146 DOI: 10.3390/jpm11111172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 01/05/2023] Open
Abstract
Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.
Collapse
|