Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases. A computer-aided diagnosis system is proposed for detecting retina abnormalities, which combines K means-based segmentation of the retina image, after due preprocessing, followed by machine learning techniques, using several low level and statistical features. Abnormalities in the retina that are classified are caused by age-related macular degeneration and diabetic retinopathy. Performance measures used in the analysis are accuracy, sensitivity, specificity, F-measure, and Mathew correlation coefficient. A comparison with another machine learning technique, the Naïve Bayes classifier shows that the classification achieved by Random Forest classifier is 93.58% and it outperforms Naïve Bayes classifier which yields an accuracy of 83.63%. Graphical abstract Random Forest classifier for abnormality detection in retina images.

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This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naïve Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naïve Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13 % and the classification accuracy of single layer perceptron for bright lesion class is 95.13 % with optimal feature set.

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In this paper, we present a retina abnormality classification framework for diabetic retinopathy and age related macular degeneration using content based image retrieval. This is performed in two phases, namely, feature extraction and pattern recognition. In the first phase, image pre-processing and Otsu multi-level thresholding is applied to retina fundus images to extract eleven low level spatial and statistical features. The second phase consists of machine learning based classification with these features using four machine learning classifiers, namely, Naive Bayes classifier, support vector machine, K-nearest neighbour classifier and random forest classifier. It is found that random forest classifier outperforms all the other classifiers for the detection of both bright and dark lesion classes and achieves 94.8% and 95.1% accuracy, respectively with ROC area 0.977 and 0.98, respectively.