Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As diabetic maculopathy (DM) is a prevalent cause of blindness in the world, it is increasingly important to use automated techniques for the early detection of the disease. ,  propose a decision system to classify DM fundus images into normal, clinically significant macular edema (CMSE), and non-clinically significant macular edema (non-CMSE) classes. The objective of the proposed decision system is three fold namely, to automatically extract textural features (both region specific and global), to effectively choose subset of discriminatory features, and to classify DM fundus images to their corresponding class of disease severity. The system uses a gamut of textural features and an ensemble classifier derived from four popular classifiers such as the hidden naive Bayes, naive Bayes, sequential minimal optimization (SMO), and the tree-based J48 classifiers.  achieved an average classification accuracy of 96.7% using five-fold cross validation.
['Pradeep Chowriappa', 'Sumeet Dua', 'U. Rajendra Acharya', 'M. Muthu Rama Krishnan']
I am writing a research paper on a idea Ensemble selection for feature-based classification of diabetic maculopathy images
[{"from": "human", "value": "I am writing a research paper on a idea Ensemble selection for feature-based classification of diabetic maculopathy images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: As diabetic maculopathy (DM) is a prevalent cause of blindness in the world, it is increasingly important to use automated techniques for the early detection of the disease. ,  propose a decision system to classify DM fundus images into normal, clinically significant macular edema (CMSE), and non-clinically significant macular edema (non-CMSE) classes. The objective of the proposed decision system is three fold namely, to automatically extract textural features (both region specific and global), to effectively choose subset of discriminatory features, and to classify DM fundus images to their corresponding class of disease severity. The system uses a gamut of textural features and an ensemble classifier derived from four popular classifiers such as the hidden naive Bayes, naive Bayes, sequential minimal optimization (SMO), and the tree-based J48 classifiers.  achieved an average classification accuracy of 96.7% using five-fold cross validation."}]