International Journal of Computer Science and Humanitarian AI

Two-Dimensional Segmentation to Reconstruct Three-Dimensional Covid-19 Patient’s Lung CT Using Active Contour

2025-01-08T23:11:22+00:00

Beginning in December 2019, SArS-CoV-2, also referred to as COVID-19, quickly spread over the world. With two recurrent waves and a 3.3% fatality rate, COVID-19 has caused over 4 million cases in Indonesia. RT-PCR, antigen, and RT-LAMP are currently the main techniques for COVID-19 detection and diagnosis. A CT scan is usually used for additional diagnosis when RT-PCR results are uncertain, but extra confirmation is required. The need to inform patients about the effects of COVID-19 on the lungs is increasing as the number of cases of the virus keeps rising and diagnosis and first aid techniques advance. The severity of COVID-19-induced pneumonia, which shows up as ground-glass opacities (GGO), which are gray patches in the lung cavity, may be seen on a single-slice CT scan. The degree of lung injury can be measured using image processing techniques. In this study, two- and three-dimensional representations of the lungs were created utilizing a multi-slice CT scan and image processing techniques like active contour and marching cubes. The suggested approach produced an average volume difference of 5% and an accuracy of 62% based on intersection over union (IoU).

Smoker Melanosis Classification Using Oral Photographic Feature Extraction Based On K-Nearest Neighbor

2025-02-20T10:11:24+00:00

Smoking is one of the causes of various diseases in the body. Smoking can also cause abnormal conditions that are pathological and physiological in the oral cavity, one of which is smoker melanosis. The clinical picture of pigmentation smoker melanosis is the presence of scattered brown spots with a diameter of less than 1 cm and is most often located on the gingiva. The data was taken using the oral photograph image capture method using a 12MP resolution camera, provided that the object distance from the camera was 6 cm and the flash was on. This analysis utilized the Gingiva Pigmentation Index (GPI) classification system proposed by Hedin, which compares the pigmented area, and Dummett's Oral Colour Index (DOPI), which assesses the density of pigmentation. In this study, the classification process was carried out with the KNN algorithm using features from digital image processing in the segmentation area, the average value of the red, green, and blue colour levels. The classification process uses the nearest neighbour value of 3 and a p-value of 2 to measure the distance to the nearest neighbor using the Minkowski distance formula. The results of the test data accuracy (1.0) with F1 scores for each class for test data DOPI 0 = 1.0, DOPI 1 = 1.0, DOPI 2 = 1.0, DOPI 3 = 1.0. Meanwhile, the classification process can use more up-to-date methods, such as CNN to improve classification accuracy.

Editorial, Foreword, and Table of Content

2025-02-20T20:08:42+00:00