Early Recognition and Grading of Cataract Using a Combined Log Gabor/Discrete Wavelet Transform with ANN and SVM

Eyes are considered to be the most sensitive and
important organ for human being. Thus, any eye disorder will affect
the patient in all aspects of life. Cataract is one of those eye disorders
that lead to blindness if not treated correctly and quickly. This paper
demonstrates a model for automatic detection, classification, and
grading of cataracts based on image processing techniques and
artificial intelligence. The proposed system is developed to ease the
cataract diagnosis process for both ophthalmologists and patients.
The wavelet transform combined with 2D Log Gabor Wavelet
transform was used as feature extraction techniques for a dataset of
120 eye images followed by a classification process that classified the
image set into three classes; normal, early, and advanced stage. A
comparison between the two used classifiers, the support vector
machine SVM and the artificial neural network ANN were done for
the same dataset of 120 eye images. It was concluded that SVM gave
better results than ANN. SVM success rate result was 96.8%
accuracy where ANN success rate result was 92.3% accuracy.

Authors:

• Hadeer R. M. Tawfik
• Rania A. K. Birry
• Amani A. Saad

Keywords:

• Cataract
• classification
• detection
• feature extraction
• grading
• log-gabor
• neural networks
• support vector machines
• wavelet.

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