Abstract
Glaucoma is a serious condition of the optic nerve, resulting in loss of eyesight. A sight-threatening disease, it causes impairment of the optic nerve. Glaucoma detection is an important field of medical image analysis. Nowadays, the scope of machine learning in medical image analysis as well as glaucoma detection is drawing attention from the clinical point of view. The ophthalmologist can avail the benefit of one more opinion through computer-aided diagnosis. The algorithm of machine learning can provide accuracy that is one step ahead for detection of glaucoma. Hence from the accuracy point of view, machine learning techniques can be a beneficial experience for the ophthalmologist. Identification of symptoms for glaucoma and glaucoma affected retinal images is a very important task. Machine learning algorithm works on the symptoms and based on the algorithm, it can classify glaucomatous eyes. Machine learning algorithm needs relevant features for identifying the symptom of glaucoma. For extracting accurate features, good quality image is important. Hence, here we mainly focus on the approach used by machine learning techniques for automatic detection of glaucoma. Retinal image acquisition, image preprocessing techniques, feature extraction, classification of symptoms of glaucoma affected image, and evaluating the performance are the important steps in machine learning techniques. Deep learning approach is an also elaborate process for detection of glaucoma in retinal images.
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Singh, L.K., Garg, H., Pooja (2020). Automated Glaucoma Type Identification Using Machine Learning or Deep Learning Techniques. In: Verma, O., Roy, S., Pandey, S., Mittal, M. (eds) Advancement of Machine Intelligence in Interactive Medical Image Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1100-4_12
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DOI: https://doi.org/10.1007/978-981-15-1100-4_12
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