Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

Abstract

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

“…To evaluate the performance and feasibility of the CS-ResCNN model in detail, we employed four representative feature extraction methods [27, 29] (LBP, WT, SIFT, and COTE), two excellent classifiers [support vector machine (SVM) and random forest (RF)] and three data-level methods [18, 19, 22] [the synthetic minority oversampling technique (SMOTE), borderline-SMOTE (BSMOTE) and under-sampling (UNDER)] for comparison. To achieve the optimal performance of the conventional methods, we firstly presented detailed parameters for classifiers, feature extraction methods and data-level methods as shown in Table 2.…”

“…To achieve the optimal performance of the conventional methods, we firstly presented detailed parameters for classifiers, feature extraction methods and data-level methods as shown in Table 2. Specifically, we chose the parameters of the feature extraction methods and classifiers based on our previous research [27–29]. For the data-level methods (SMOTE, borderline-SMOTE and UNDER), we mainly referred to the previous studies [18, 19, 22] and their open source codes.…”

“…By contrast, the cost-sensitive approach using algorithm leveling has a distinct advantage because it makes full use of the original data [9, 21, 22]. Meanwhile, deep convolutional neural network (CNN) models have demonstrated extraordinary performance in medical image recognition tasks [24–29]. In this study, we combine a representative deep learning CNN (deep residual network [30]) and a cost-sensitive data-balancing method to present an effective cost-sensitive residual CNN (CS-ResCNN) for the ophthalmic imbalanced dataset problem.…”

Application of machine learning in ophthalmic imaging modalities

“…In addition, some DL methods for grading and classifying slit-lamp images have shown effective results [106,107]. Lin and colleagues' team developed a prototype diagnostic and therapeutic system (CC-Cruiser) for pediatric cataract screening by using preprocessed ocular images and a DCNN [108]; they compared the performances of multiple DL and conventional ML methods from various perspectives [109,110]. CC-Cruiser has been used in the Ophthalmic Center of Sun Yat-sen University with an accuracy comparable to that of ophthalmologists.…”

A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification

“…The authors in (Wang et al, 2017) present a comparative study of image classification techniques for automatic diagnosing ophthalmic diseases. In this study, typical methods for feature extraction were combined with various classification techniques in different schemas to identify ophthalmic diseases.…”