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All Journal Jurnal Keperawatan Padjadjaran Journal of Information Technology and Computer Engineering
Risalina Myrtha
Universitas Sebelas Maret
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Neuroscience Nursing Public Health

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Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer Febri Liantoni; Coana Sukmagautama; Risalina Myrtha
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 01 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.675 KB) | DOI: 10.25077/jitce.4.01.40-44.2020

Abstract

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.
Can D-dimer predict length of hospital stay in COVID-19 survivors? A cross-sectional study Matthew Aldo Wijayanto; Risalina Myrtha; Dwi Rahayu; Graciella Angelica Lukas
Jurnal Keperawatan Padjadjaran Vol. 11 No. 2 (2023): Jurnal Keperawatan Padjadjaran
Publisher : Faculty of Nursing Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jkp.v11i2.2245

Abstract

Background: COVID-19 has been shown to increase the risk of thrombosis, where this mechanism occurs due to cell damage that triggers the release of various proinflammatory cytokines and chemokines, thereby activating the coagulation cascade. Thus, an increase D-dimer levels in COVID-19 patients occurs. The duration of patients' hospitalization, known as Length of Hospital Stay (LOS), plays a crucial role in enhancing patient care, reducing overall costs, and optimizing resource allocation. Purpose: The main objective of this study is to determine the correlation between D-dimer and various other factors to assess its predictive value for LOS) in COVID-19 survivors. Methods: This observational analytic study included COVID-19 patients who were admitted to Universitas Sebelas Maret Hospital in Sukoharjo, Indonesia, from November 2020 to January 2021. The data was taken from the medical records of patients diagnosed with COVID-19. Age, gender, comorbidities, admission oxygen saturation, D-dimer, neutrophil-lymphocyte ratio (NLR), haemoglobin, platelet count, white blood cells (WBC), LOS and estimated glomerular filtration rate (eGFR) were analysed in this study. Binary logistic regression was applied to determine the correlation between potential predictors on LOS. Results: A total 104 patients were included in the final analysis. The median LOS was 13 days (IQR 9-17 days). There was an increase of D-dimer in 79 patients with the median 759.39 ng/ml. Patients with prolonged LOS tend to have higher D-dimer levels (Median 924.95 vs 591.54 ng/ml, p = 0.018). However, D-dimer and other parameters was not associated with prolonged LOS in COVID-19 survivors (D-dimer p = 0.188; Age p = 0.138; Diabetes mellitus p = 0.172; NLR p = 0.859; Platelet count p = 0.097). Conclusions: D-dimer levels does not accurately predict prolonged LOS in COVID-19 survivors. Therefore, we suggest D-dimer solely should not be used as a tool to predict patient’s LOS.
Co-Authors Coana Sukmagautama Dwi Rahayu Febri Liantoni Graciella Angelica Lukas Matthew Aldo Wijayanto