SEGMENTASI EXUDATE PADA CITRA RETINA PENYAKIT DIABETIC RETINOPATHY MENGGUNAKAN MODIFIKASI ARSITEKTUR U-NET, DENSE BLOCK, DAN CONVOLUTIONAL LONG SHORT-TERM MEMORY

AYUPUTRI, NIKEN and Desiani, Anita and Resti, Yulia (2025) SEGMENTASI EXUDATE PADA CITRA RETINA PENYAKIT DIABETIC RETINOPATHY MENGGUNAKAN MODIFIKASI ARSITEKTUR U-NET, DENSE BLOCK, DAN CONVOLUTIONAL LONG SHORT-TERM MEMORY. Undergraduate thesis, Sriwijaya University.

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Abstract

Diabetic retinopathy (DR) is an eye disease caused by high glucose and blood pressure levels that can lead to blindness. DR can be detected through the presence of exudates, which are white or yellow spots that form due to damage to the blood vessels of the retina. The process of image segmentation is performed to obtain exudate features in the retinal image. Automatic segmentation development can use the Convolutional Neural Network (CNN) method. U-Net is a CNN architecture that is capable of segmentation because it has encoder and decoder parts. Encoder functions to extract features but has the risk of losing fine image features and vanishing gradient due to the down sampling process. The decoder functions to reconstruct the image but has the risk of losing important information due to skip connection.This research modifies the U-Net architecture by adding Dense Block and Convolutional Long Short-Term Memory (ConvLSTM) for exudate segmentation in retinal images. Dense Block is applied to the encoder part to retain the fine features of the image and overcome the vanishing gradient problem. ConvLSTM is inserted in the decoder to retain important information in the image. This research performs image segmentation with two labels, namely exudate and background. The exudate segmentation results with the application of the proposed architecture obtained an accuracy of 99%, indicating the model is very good at predicting all labels correctly as a whole. Sensitivity of 82%, indicating the model predicts the exudate area well. Specificity of 99%, indicating the model is very good at predicting areas that are not part of the exudate. F1-Score 75%, indicating the model has a good balance between sensitivity and specificity. IoU 61%, indicating a poor level of overlap between the prediction results and the ground truth. The results of this study show that the model predicts and performs well in segmenting exudates in retinal images. However, improvements in sensitivity, F1-Score and IoU are required to optimize the performance of the segmentation model.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	ConvLSTM, Dense Block, Exudate, Segmentasi, U-Net
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Niken Ayuputri
Date Deposited:	25 May 2025 03:25
Last Modified:	25 May 2025 03:25
URI:	http://repository.unsri.ac.id/id/eprint/174031

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