Diagnosis of Diverse Retinal Disorders Using a Multi-Label
Computer-Aided System
Volume 2 - Issue 3
Mohammed SI Alabshihy1, A Abdel Maksoud2, Mohammed Elmogy3, S Barakat2 and Farid A Badria4*
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- 1Specialist of Ophthalmology, Al Azhar University, Egypt
- 2Information Systems Department, Faculty of Computers and Information, Mansoura University, Egypt
- 3Information Technology Department, Faculty of Computers and Information, Mansoura University, Egypt
- 4Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Egypt
*Corresponding author:
Farid A Badria, Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Egypt
Received:June 24, 2019; Published:July 09, 2019
DOI:
10.32474/TOOAJ.2018.02.000139
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Abstract
Multi-label classification has a great importance in medical data analysis. It means that each sample can associate with more
than one class label. Therefore, it represents complex objects by labeling some basic and hidden patterns. The patient may have
multiple diseases at one organ, such as the retina, at the same time. The ophthalmological diseases such as diabetic retinopathy (DR)
and hypertension, can cause blindness if not classified accurately. The physician can diagnose one disease but may omit the other.
Therefore, multi-label classification is essential to handle this situation, which is challengeable by nature. It has many problems,
such as high dimensionality and labels dependency. In addition, the retinal image is very difficult to be analyzed as some contents of
it has the same features of the diseases. It suffers from the changes of brightness, poor contrast, and noise. In this paper, we detect
multiple signs of DR disease at the same time. We introduce a multi-label computer-aided diagnosis (ML-CAD) system. We used the
concept of problem transformation to allow more extensions to improve the results. We used multi-class support vector machine
(MSVM) to decompose the problem into a set of binary problems. We evaluated our method due to the accuracy and training
time. We made comparisons among the proposed ML-CAD and the other state-of-the-art classifiers using DIARETDB dataset. The
experiments proved that this method outperforms the others in accuracy.
Keywords: Multi-label Classification; Ophthalmological Diseases; Multi-Label Computer Aided Diagnosis (ML-CAD) system
Abbreviations: Multi-Label Computer-Aided Diagnosis (ML-CAD); Diabetic retinopathy (DR); Fluorescein angiography (FA);
Optic disc (OD); Macula (MA); Fovea (FV); Region Of interest (ROI); Multi-class Support Vector Machine (MSVM); Ground Truth
(GT); Circular Hough Transform (CHT); Contrast Limited Adaptive Histogram Equalization (CLAHE); Root Mean Square (RMS); Gray
Level Co-occurrence Matrix (GLCM)
Abstract|
Introduction|
Basic Concepts|
The Related Work|
The Related Work Results and Discussions|
The The Discussion|
The Conclusion|
References|
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