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Browsing MS DEPARTMENT OF INFORMATION SYSTEM by Author "FATIMA GHANI"

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    A METHODOLOGY FOR GLAUCOMA DISEASE DETECTION USING DEEP LEARNING TECHNIQUES
    (UMT, Lahore, 2020) FATIMA GHANI
    The main source of the glaucoma is irreversible impairment of vision. In literature we reviewed many methods to machine learning used on fundus pictures by different researchers. Any current machine learning solutions include C4.5, the Naïve Bayes Classifier, and Random Wood. Many methods cannot more reliably diagnose glaucoma disorder. We developed an architecture focused on the methodology of Deep Learning ( DL) which is a Convolution Neural Network (CNN) for the classification of Glaucoma diseases. We used numerous deep learning neural networks such as the Inception-V3 and the Vgg16 model for Glaucoma classification and identification purposes. We have obtained 508 fundus photos belonging to 25 groups from the JSIEC, Shantou City, Guangdong Province , China, Joint Shantou Foreign Eye Centre. Since uploading the photos, we've applied the increase to the provided dataset and rendered the 1563 training and testing data collection pictures. The downloaded dataset is not labelled, so we wanted a named picture dataset for our research in deep learning. But we have labelled both photos with the class name of the disease after the augmentation. We also used two deep neural network models Inception V-3 and Vgg16 in this paper which are supervised learning methods for classification arrangements. Such structures require operating processes that need to learn to use previous knowledge , make judgments about it and fix it if any errors arise. Taking into consideration the success findings collected, it is shown that the pre-trained Inception V-3 model has the best classification efficiency with 90.01% accuracy for two other models suggested (90.01% accuracy for InceptionV3 and 83.46% accuracy for Vgg16).