Abstract:
Cataract is the leading cause of blindness worldwide. Accurate and convenient cataract detection and severity evaluation will help. This paper proposes automatic cataract detection and grading.
With prior knowledge, improved Haar and visible structure features are combined as features, and multilayer perceptron with discrete state transition (DST-MLP) or exponential DST (EDST-MLP) classifiers are designed.
DST- or EDST-residual neural networks are proposed without prior knowledge. Our DST and EDST strategies prevent overfitting and reduce storage memory during network training and implementation, resulting in state-of-the-art cataract detection and grading accuracy.
The experimental results show that combined features always outperform single features, and classification methods with feature extraction based on prior knowledge are better for complex medical image classification. These analyses can inform medical image processing applications.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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