By: Gagana D B, Sanjana K M, Sinchana S, Sneha C M, and Arjun U
1-Student, Department of Computer Science and Engineering, PES Institute of Technology and Management Shivamogga, Karnataka
2-Student, Department of Computer Science and Engineering, PES Institute of Technology and Management Shivamogga, Karnataka
3-Student, Department of Computer Science and Engineering, PES Institute of Technology and Management Shivamogga, Karnataka
4-Student, Department of Computer Science and Engineering, PES Institute of Technology and Management Shivamogga, Karnataka
5-Associate Professor and Head, Department of Computer Science and Engineering PES Institute of Technology and Management, Shivamogga, Karnataka
Maculopathies, a diverse group of eye diseases affecting the central portion of the retina, the macula, are a leading cause of vision loss globally. Early detection and accurate diagnosis are critical for timely intervention and preventing irreversible vision loss. However, traditional methods such as manually detecting the disease are time-consuming, tiring, and often lack sensitivity and specificity, hindering optimal patient care. Here we delve into the applications of machine learning and other deep learning algorithms for analyzing retinal images including optical coherence tomography (OCT). We explore how machine learning algorithms can automatically extract subtle features and patterns from retinal images, enabling accurate differentiation between healthy and diseased maculas, as well as classifying specific maculopathy conditions. This work showcases the most common retinal diseases, provides an overview of the prevalent imaging modalities, and presents a critical evaluation of current deep-learning research for the detection and diagnosis of drusen, diabetic macular edema (DME), and choroidal neovascularization (CNV). For the model, we proposed a residual neural network (ResNet), a convolutional neural network that allows high performance in image recognition. The deep learning model is structured so that learning takes place on a hierarchical set of representations and to update the network weights more efficiently we used the Adam (adaptive moment estimation) optimization algorithm, which is an extension of the Stochastic Gradient Descent (SGD) and is used instead of classical SGD. Overall, all the methods represent a significant advancement in disease detection and diagnosis using OCTs, and have the great potential to assist healthcare professionals and clinicians in informed decision-making, the early intervention of disease, and personalized treatment optimization.
Keywords: Maculopathy diagnosis, Optical Coherence Tomography (OCT), Machine Learning, Drusen, DME, CNV, ResNet.
Citation:
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