AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learni...AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learning techniques.METHODS:Fundus images from the IDRiD dataset and additional Kaggle datasets were used.A wavelet-based band-pass filter was applied for edge enhancement of retinal features.Gaussian mixture model(GMM)clustering was used to segment and extract texture features.These extracted features were classified using machine learning algorithms,including a random forest classifier and a multilayer perceptron neural network.Performance metrics such as sensitivity,specificity,and accuracy were computed to evaluate the proposed model’s diagnostic effectiveness.RESULTS:The random forest-based classification system achieved a sensitivity of 95.08%,specificity of 86.67%,and overall accuracy of 95.20%in detecting DR lesions.The combination of wavelet-based edge enhancement,GMM clustering,and neural network-based feature classification demonstrated high reliability in lesion identification.CONCLUSION:The proposed method effectively detects early signs of DR from fundus images,offering a highaccuracy,automated,and scalable solution for assisting ophthalmologists.Its application can support large-scale screening programs,particularly in regions with limited access to specialized eye care.展开更多
文摘AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learning techniques.METHODS:Fundus images from the IDRiD dataset and additional Kaggle datasets were used.A wavelet-based band-pass filter was applied for edge enhancement of retinal features.Gaussian mixture model(GMM)clustering was used to segment and extract texture features.These extracted features were classified using machine learning algorithms,including a random forest classifier and a multilayer perceptron neural network.Performance metrics such as sensitivity,specificity,and accuracy were computed to evaluate the proposed model’s diagnostic effectiveness.RESULTS:The random forest-based classification system achieved a sensitivity of 95.08%,specificity of 86.67%,and overall accuracy of 95.20%in detecting DR lesions.The combination of wavelet-based edge enhancement,GMM clustering,and neural network-based feature classification demonstrated high reliability in lesion identification.CONCLUSION:The proposed method effectively detects early signs of DR from fundus images,offering a highaccuracy,automated,and scalable solution for assisting ophthalmologists.Its application can support large-scale screening programs,particularly in regions with limited access to specialized eye care.
