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Dwiza Riana
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Journal Medical Informatics Technology
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 29887003     EISSN : 29887003     DOI : https://doi.org/10.37034/medinftech
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health
Journal Medical Informatics Technology publishes papers on innovative applications, development of new technologies and efficient solutions in Health Professions, Medicine, Neuroscience, Nursing, Dentistry, Immunology, Pharmacology, Toxicology, Psychology, Pharmaceutics, Medical Records, Disease Informatics, Medical Imaging and scientific research to improve knowledge and practice in the field of Medical.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 5 Documents
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Search results for , issue "Volume 1 No. 2, June 2023" : 5 Documents clear
Image Segmentation of Normal Pap Smear Thinprep using U-Net with Mobilenetv2 Encoder Deviana Sely Wita
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.6

Abstract

Pap smear is a technique to detect changes in the cells in the uterine wall. With a Pap smear, a woman can be known to have cervical cancer or not. However, the problem of cancer screening on pap smear images is largely hindered by improper cell staining and overlapping cell images. For accurate Pap smear image segmentation, this study uses the U-Net method which is better for Pap smear image segmentation. This method integrates the MobilenetV2 network and converts ordinary convolution into deep split convolution to improve transmission and feature utilization by the network, and at the same time increase the speed of feature extraction. Then the segmentation results from MobilenetV2 produce accuracy in distinguishing the nucleus, cytoplasm, and background on the pap smear image. The dataset used in this study is a normal analogue image of the Pap smear image obtained from the RepoMedUNM Database. Initial data processing is done by digitizing the image, where analog data from the Pap smear is transformed into a digital image. Based on the results of research that has been carried out, namely segmentation of Pap smear images using U-Net with MobilenetV2 encoder, the accuracy value on differences in nucleus, cytoplasm, and background cells is 98%.
Glaucoma Detection in Fundus Eye Images using Convolutional Neural Network Method with Visual Geometric Group 16 and Residual Network 50 Architecture Chandra Nugraha; Sri Hadianti
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.7

Abstract

Glaucoma is an eye disease usually caused by abnormal eye pressure. One of the causes of abnormal eye pressure is blockage of fluid flow, which if detected too late can lead to blindness. Glaucoma can be identified by examining specific areas on the retina fundus image. The aim of this study is to detect positive and negative glaucoma in fundus images. The image data was obtained from the glaucoma_detection dataset, consisting of 520 images, including 134 glaucoma-infected images and 386 normal images. This study uses the Convolutional Neural Network (CNN) method with Visual Geometric Group-16 (VGG-16) and Residual Network-50 (ResNet-50) architectures. The research and testing results using the VGG-16 architecture obtained an accuracy rate of 78%, while using the ResNet-50 architecture obtained an accuracy rate of 80%.
Classification of Myopia Levels using Deep Learning Methods on Fundus Image Waeisul Bismi; Jufriadif Na`am
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.8

Abstract

Disorders of the eye or also known as eye disease is a condition that can affect vision for some people in their lifetime. There are 40 types of eye disorders or eye diseases, one of which is Myopia. Myopia is a visual disturbance that causes objects that are far away to appear blurry, but there is no problem seeing objects that are near. Myopia or nearsightedness is also known as minus eye. From this description, it is very important to conduct research in detecting eye diseases before the increase in eye minus and blindness. This study aims to classify myopic eye disease using the Deep Learning method with several different architectures, namely the VGG16, VGG19 and InceptionV3V3 models. Where the first is to distinguish normal and abnormal while the other is to classify with Augmented myopia image dataset and non augmented myopia image dataset obtained from the Retinal Fundus Multi-Disease Image Dataset (RFMID). In the implementation of the Deep Learning method using 20 Epochs. The results of the accuracy of the classification of eye diseases using the non augmented myopia image dataset are 66.0% for the VGG16 architectural model, then 95.99% for the VGG19 architectural model and 93.99% for the InceptionV3 architectural model and the accuracy results using the Augmented myopia image dataset are 97.53% for the VGG16 architectural model, 97.53% for the VGG19 architectural model and 99.50% for the InceptionV3 architecture model.
Systematic Review: Risk Factors and Prevalence of Urinary Incontinence in Elderly Women, a Case Study in Japan and Taiwan Alya Shafira Hewiz; Novira Widajanti; Lukman Hakim; Rwahita Satyawati
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.9

Abstract

Knowledge of the conditions of elderly women in Japanese and Taiwanese communities, particularly in relation to risk factors and their association with urinary incontinence, is of interest. This study aimed to identify risk factors and prevalence of urinary incontinence in elderly women in the community of the Japanese and Taiwanese case study areas. The research method used was a systematic review based on PRISMA guidelines. Data sources were obtained from PubMed and Science Direct for the period 2000-2020 using specific inclusion and exclusion criteria. Evaluation was conducted for quality and bias risk using a standardized assessment system. Results showed that the prevalence of urinary incontinence in elderly women in Japanese and Taiwanese communities ranged from 29.8% to 31.3%. Many factors influenced urinary incontinence, such as age, body mass index (BMI), and smoking habits. From the two selected articles in Japan and Taiwan, it was concluded that urinary incontinence was commonly experienced by elderly women in the community, and awareness of this condition could help improve management.
Optimization of Melanoma Skin Cancer Detection with the Convolutional Neural Network Harming Puja Kekal; Daniati Uki Eka Saputri
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.10

Abstract

Currently, skin cancer is a very dangerous disease for humans. Skin cancer is classified into many types such as Melanoma, Basal and Squamous cell carcinoma. In all types of cancer, melanoma is the most dangerous and unpredictable disease. Detection of melanoma cancer at an early stage is useful for effective treatment and can be used to classify types of melanoma cancer. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to assist the medical and medical world in analyzing images precisely and accurately. The method used in this research is Convolutional Neural Network (CNN) with MobileNet model architecture. Skin cancer detection consists of five important stages, namely image database collection, preprocessing methods, augmentation data, model training and model evaluation. This evaluation was carried out using the MobileNet method with an accuracy of 88%.

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