Color Feature Segmentation Image for Identification of Cotton Wool Spots on Diabetic Retinopathy Fundus

Fundus is an image of the inner eye surface in the form of a colored image. This image has a lot of pixel values because it consists of three basic color components. The three colors are red, green, and blue, so they need a good technique in analyzing this image. This image can be used to diagnose diabetic retinal disease caused by diabetes mellitus. This disease can interfere with human vision because objects that cover the retina of the eye is called Cotton Wool Spot (CWS). The severity of this disease can be observed from the large area of the CWS covering the retina. This study aims to calculate the exact area ratio of CWS with the retina area. The method used in this research is Image Color Feature Segmentation (ICFS). This method has four stages, namely preprocessing, segmentation, feature extraction, and feature areas. The dataset processed in this study was sourced from the Radiology Department, General Hospital of M. Djamil Padang. The dataset consists of 16 fundus images of patients who were treated at the hospital. The results of this study can identify and calculate the percentage of retinal damage is very well. Therefore, this study can be a reference in measuring the severity of diabetic retinopathy for prevention and subsequent treatment for patients and doctors

Sistem Pakar Diagnosis Penyakit Jamur pada Manusia Menggunakan Input Suara Berbasis Android

Fungal skin disease is a type of disease that is often suffered people in the tropics. Phenomena in the community often assume that this disease will heal by itself. However, if left the effects of fungal skin disease can worsen the sufferer condition. The importance of early detection and treatment is very necessary, but this requires patients go to hospital or doctor so that patients spend a lot of time and money. For this reason, was build an android expert system with speech to early diagnose fungal skin diseases. From this initial diagnosis will save time and money and provide alternative prevention for sufferers. The method used in this study is the Certainty Factor of 20 patients. The accuracy of the test results to the system compared to the results of the doctor's diagnosis is 95%. So that, this expert system can be an early alternative in diagnose fungal skin diseases in humans.Penyakit jamur kulit merupakan jenis penyakit yang sering diderita oleh masyarakat yang bertempat tinggal pada daerah tropis. Fenomena di masyarakat sering kali beranggapan bahwa penyakit ini akan sembuh dengan sendirinya. Namun jika dibiarkan maka dampak dari penyakit jamur kulit dapat memperburuk keadaan penderita. Pentingnya pendeteksian dan pengobatan sejak dini sangat diperlukan, namun hal ini mengharuskan penderita berobat ke rumah sakit atau dokter sehingga penderita mengeluarkan biaya dan waktu yang banyak. Untuk itu dibangun sebuah sistem pakar berbasis android dengan input suara untuk mendiagnosis awal terhadap penyakit jamur kulit. Dari diagnosis awal ini akan menghemat waktu dan biaya serta memberikan alternatif pencegahan terhadap penderita. Metode yang digunakan dalam penelitian ini adalah Certainty Factor terhadap 20 pasien. Akurasi hasil pengujian terhadap sistem yang dibandingkan dengan hasil diagnosis dokter adalah 95%. Sehingga sistem pakar ini dapat menjadi alternatif awal dalam mendiagnosis penyakit jamur kulit pada manusia

Automated Identification Model of Ground-Glass Opacity in CT-Scan Image by COVID-19

Ground-Glass Opacity (GGO) is an object found in the thorax due to infection. This object interferes with the normal function of the thorax in breathing. The characteristic of GGO has slightly lighter turbidity compared to normal thorax tissue on radiological images, so it is very difficult to identify it precisely. This study aims to identify the GGO pattern and find the exact area of the CT-scan image of COVID-19 sufferers. The data tested were 34 images from 34 different patients. The image was taken using CT-Scan equipment with the tube model 46274891G1 axially. Each patient is taken one image with the reading position right above the chest using the file format Joint Photographic Experts Group (jpg). An automatic image processing model developed in this study uses several interrelated and continuous technical steps; Image Enhancement, Convert to Binary Image, Morphology Operation, Image Inverted, Active Contour Model, Image Addition, Convert Matrix to Grayscale, Image Filtering, Convert to Binary Image, Image Subtraction and Region Properties. The results of this study can identify GGO in all patient test images, where each patient has GGO. The smallest area of GGO was 3.9%, and the highest was 34.2% of the total thorax area. This level of comparison is greatly influenced by the severity of the COVID-19 virus patient. This area of GGO weakens the normal function of the thorax in the respiratory process of the patient. Thus, this research can be used as a model recommendation in identifying thorax damage due to COVID-19 very well in following up on more intensive treatment in the future