研究目的
Early detection of Diabetic Retinopathy (DR) through the examination of retinoscopy images to prevent blindness.
研究成果
The proposed methodology showed notable improvement in detecting Diabetic Retinopathy with high accuracy rates using KNN and SVM classifiers, suggesting its potential for early detection and prevention of blindness caused by DR.
研究不足
The study does not mention the computational resources required for processing or the scalability of the proposed methodology to larger datasets.
1：Experimental Design and Method Selection:
The methodology involved pre-processing of images using histogram equalization, application of Discrete Wavelet Transform (DWT) for feature extraction, and classification using KNN and SVM classifiers.
2：Sample Selection and Data Sources:
The MESSIDOR dataset, consisting of 1,200 fundus eye images with varying levels of DR, was used.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
Images were pre-processed, transformed using DWT, features were extracted, and then classified.
5：Data Analysis Methods:
Performance was evaluated based on sensitivity, specificity, and accuracy.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
