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All Journal ComEngApp : Computer Engineering and Applications Journal Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING SEINASI-KESI Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informasi dan Teknologi Jurnal Digital Teknologi Informasi Jurnal Generic Informanika Jurnal Pengabdian Kepada Masyarakat Inovasi Teknologi The Indonesian Journal of Computer Science
Kesuma, Lucky Indra
Universitas Muhammadiyah
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images Darmo, Cahyo Pambudi; Kesuma, Lucky Indra; Geovani, Dite
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.
Combination Of Gamma Correction and Vision Transformer In Lung Infection Classification On CT-Scan Images Kesuma, Lucky Indra; Octavia , Pipin; Sari , Purwita; Batubara, Gracia Mianda Caroline; Karina, Karina
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.588

Abstract

Lung infection is an inflammatory condition of the lungs with a high mortality rate. Lung infections can be identified using CT-Scan images, where the affected areas are analyzed to determine the infection type. However, manual interpretation of CT-Scan results by medical specialists is often time-consuming, subjective, and requires a high level of accuracy. To address these challenges, this study proposes an automated classification method for lung infections using deep learning techniques. Convolutional Neural Networks (CNNs) are widely used for image classification tasks. However, CNN operates locally with limited receptive fields, making capturing global patterns in complex lung CT images challenging. CNN also struggles to model long-range pixel dependencies, which is crucial for analyzing visually similar regions in lung CT-Scans. This study uses a Vision Transformer (ViT) to overcome CNN limitations. ViT employs self-attention mechanisms to capture global dependencies across the entire image. The main contribution of this study is the implementation of ViT to enhance classification performance in lung CT-Scan images by capturing complex and global image patterns that CNN fails to model. However, ViT requires a large dataset to perform optimally. To overcome these challenges, augmentation techniques such as flipping, rotation, and gamma correction are applied to increase the amount of data without altering the important features. The dataset comprises lung CT-scan images sourced from Kaggle and is divided into Covid and Non-Covid classes. The proposed method demonstrated excellent classification performance, achieving accuracy, sensitivity, specificity, precision, and F1-Score above 90%. Additionally, the Cohen’s kappa coefficient reached 89%. These results show that the proposed method effectively classifies lung infections using CT-Scan images and has strong potential as a clinical decision-support tool, particularly in reducing diagnostic time and improving consistency in medical evaluations.
Co-Authors Ade Sukmawati Ade Sukmawati Ahmad Fali Oklilas Ahmad Rifai Airlambang, Dwiki Alamsyah, Agung Anggara, M Ikhsan Azhar Andika Putra BAKHTIAR K Batubara, Gracia Mianda Caroline Bayu Wijaya Putra Buchari, M. Ali Darmo, Cahyo Pambudi Dite Geovani Dwi Fitri Brianna Erly Krisnanik Fitri Brianna, Dwi Frans Sudirjo Geovani, Dite Intan Hesti Indriana Iryani, Lemi Karina Karina Lemi Iryana M. Ali Buchari Marissa Cristianti Mira Afrina Muhammad Ikhsan Anggara Naraloka, Therezia Nia Umilizah Octavia , Pipin Octavia, Pipin PAISAL PAISAL Pandi Adriansyah Purwita Sari Purwita Sari Purwita Sari, Purwita Puspa Sari, Puspa Putri Maharani Ratna Tungga Dewa, Dominica Maria Rayani, Ira Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah Rudiansyah, Rudiansyah Sari , Purwita Suedarmin, Muhammad Suryaningsih, Lilik Therezia Naraloka Tuti Ayu Lestari Umilizah, Nia Wiwik Handayani Yuniarti Utami, Eva