Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images Dwi Fitri Brianna, Lucky Indra Kesuma, Dite Geovani, Puspa Sari Journal of Electronics Electromedical Engineering and Medical Informatics, 2025 Liver cancer can be identified using CT-Scan liver image segmentation. Liver segmentation can be performed using CNN architecture like U-Net. However, the segmentation results using U-Net architecture are affected by image quality. Low image quality can affect the accuracy of segmentation results. This study proposes a combination of image enhancement and segmentation stages on CT-Scan liver images. Image enhancement is achieved by using a combination of CLAHE to enhance contrast and Bilateral Filter to reduce noise. The segmentation architecture proposed in this study is Double U-Net which is a development of U-Net architecture by adding a second U-Net block with the same structure as a single U-Net. The first U-Net is used to extract simple features, while the second U-Net is used to extract more complex features and enhance the segmentation results of the first U-Net. PSNR and SSIM measure the results of image enhancement. The PSNR is more than 40dB and the SSIM result is close to 1. These results show that the proposed image enhancement method can enhance the quality of original images. The segmentation results were measured by calculating accuracy, sensitivity, specificity, dice score, and IoU. The result of liver segmentation obtained 99% for accuracy, 98% for sensitivity, 99% for specificity, 98% for dice score, and 90% for IoU. This shows that liver segmentation using Double U-Net obtained good segmentation. Results of image enhancement and image segmentation show that the proposed method is very good for enhancing image quality and performing liver segmentation accurately.
Enhanced Palm Oil Image Quality Using RGF-ESRGAN Architecture Arif Fadillah, Anita Desiani, Dian Palupi Rini, Lucky Indra Kesuma, Nuni Gofar, et al. Proceedings 7th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2025, 2025
Median Filter and U-Net Architecture for Robust Segmentation Nucleus and Cytoplasm on Pap Smear Rudiansyah, Anita Desiani, Dian Palupi Rini, Lucky Indra Kesuma, Fitri Salamah, et al. Proceedings 6th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2024, 2024 Cervical cancer is a condition caused by a layer of malignant cells that grows and develops rapidly on the cervix due to infection with the human papillomavirus virus (HPV). Cancer detection of the cervix can be done by a pap smear examination. This study aims to build an automatic segmentation model by combining augmentation and segmentation. The augmentation techniques used in this study are flip and median filters. Augmentation techniques aim to increase the quantity and variation of data to improve the quality of segmentation results. U-Net architecture is often used for image segmentation. By combining data augmentation and U-NET architecture, it is expected to meet the needs of this model which requires a lot of data. A combination of augmentation with U-NET is anticipated to improve the performance of the model significantly. The parameters used to measure the performance of the proposed method include accuracy, precision, recall, and Intersection over Union (IoU). The results of this method show an accuracy of 92%, precision of 91%, recall of 90%, and IoU of 84%. The results of the performance evaluation show that the method proposed for pap smear segmentation has excellent and powerful capabilities. However, the IoU performance in this study needs to be improved for further study. The proposed method can be used as a model for the development of automatic cervical cancer detection applications in the medical field.
Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods Lucky Indra Kesuma, Ermatita Ermatita, Erwin Erwin, Purwita Sari, Rudhy Ho Purabaya Proceedings 4th International Conference on Informatics Multimedia Cyber and Information System Icimcis 2022, 2022 The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well.
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Perancangan Basis Data D Anggoro, P Sari, LI Kesuma, Y Sonatha, MF Rustan, L Faizal Get Press , 2023 2023
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Application of the user centered design method to evaluate the relationship between user experience, user interface and customer satisfaction on banking mobile application S Frans, MRTD Dominica, IK Lucky, S Lilik, YU Eva Jurnal Informasi Dan Teknologi 6 (1) , 2024 2024 Citations: 50
ELREI: Ensemble Learning of ResNet, EfficientNet, and Inception-v3 for Lung Disease Classification based on Chest X-Ray Image. LI Kesuma International Journal of Intelligent Engineering & Systems 16 (5) , 2023 2023 Citations: 27
Classification of COVID-19 diseases through lung CT-scan image using the ResNet-50 architecture LI Kesuma Computer Engineering and Applications Journal 12 (1), 11-30 , 2023 2023 Citations: 17
Arsitektur U-Net pada Segmentasi Citra Hati sebagai Deteksi Dini Kanker Liver. T Naraloka, LI Kesuma, A Sukmawati, M Cristianti Techno. com 21 (4) , 2022 2022 Citations: 16
Combination of image enhancement and u-net architecture for cervical cell semantic segmentation R Rudiansyah, L Iryani, LI Kesuma, P Sari, A Alamsyah Journal of Informatics and Telecommunication Engineering 7 (2), 575-586 , 2024 2024 Citations: 14
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Identification of Floods in Palembang Area Using Fuzzy Logic Method of Mamdani and Sugeno A Sukmawati, L Iryana, P Adriansyah, LI Kesuma Journal Of Informatics And Telecommunication Engineering 6 (2), 434-444 , 2023 2023 Citations: 12
Improved chest X-ray image quality using median and gaussian filter methods LI Kesuma, E Ermatita, E Erwin, P Sari, RH Purabaya 2022 International Conference on Informatics, Multimedia, Cyber and … , 2022 2022 Citations: 12
Implementasi Metode Multistage Random Sampling untuk Aplikasi Quick Count pada Pilkada Kota Palembang Berbasis Java Mobile P Sari, LI Kesuma, A Rifai J. Ilmu Komput. dan Teknol. Inf 1 (1), 10-15 , 2021 2021 Citations: 12
Biometric fingerprint implementation for presence checking and room access control system S Muslimin, Y Wijanarko, LI Kesuma, R Maulidda, Y Hasan, H Basri 4th Forum in Research, Science, and Technology (FIRST-T1-T2-2020), 490-494 , 2021 2021 Citations: 11
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Reliabilitas Instrumen Kualitas E-Learning Menggunakan Teori Whyte & Bytheway Dan Webqul 4.0 LI Kesuma Jurnal Digital Teknologi Informasi 2 (2), 94-98 , 2019 2019 Citations: 8
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Decision Support System for Determining COVID-19 Aid Recipients using the Simple Additive Weighting (SAW) Method LI Kesuma, I Indra, I Irmawati Journal Of Engineering And Technology Innovation (JETI) 2 (01), 18-25 , 2023 2023 Citations: 6
LITERASI DIGITAL: CERDAS MENGGUNAKAN MEDIA SOSIAL DALAM MENANGGULANGI BERITA PALSU (HOAX) DILINGKUNGKAN UNIVERSITAS SJAKHYAKIRTI AA Putra, P Maharani, LI Kesuma Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi 1 (01), 13-17 , 2023 2023 Citations: 6
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Sistem Informasi Sekolah SPS PAud Balkam Ceria Berbasis Website D Airlambang Universitas Pembangunan Nasional Veteran Jakarta , 2022 2022 Citations: 6