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All Journal JOIV : International Journal on Informatics Visualization The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Aniati Murni
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Computer Science & IT Earth & Planetary Sciences

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SCOV-CNN: A Simple CNN Architecture for COVID-19 Identification Based on the CT Images Haryanto, Toto; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Yusoff, Marina; Zain, Jasni Mohammad
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1750

Abstract

Since the coronavirus was first discovered in Wuhan, it has widely spread and was finally declared a global pandemic by the WHO. Image processing plays an essential role in examining the lungs of affected patients. Computed Tomography (CT) and X-ray images have been popularly used to examine the lungs of COVID-19 patients. This research aims to design a simple Convolution Neural Network (CNN) architecture called SCOV-CNN for the classification of the virus based on CT images and implementation on the web-based application. The data used in this work were CT images of 120 patients from hospitals in Brazil. SCOV-CNN was inspired by the LeNet architecture, but it has a deeper convolution and pooling layer structure. Combining seven and five kernel sizes for convolution and padding schemes can preserve the feature information from the images.  Furthermore, it has three fully connected (FC) layers with a dropout of 0.3 on each. In addition, the model was evaluated using the sensitivity, specificity, precision, F1 score, and ROC curve values. The results showed that the architecture we proposed was comparable to some prominent deep learning techniques in terms of accuracy (0.96), precision (0.98), and F1 score (0.95). The best model was integrated into a website-based system to help and facilitate the users' activities. We use Python Flask Pam tools as a web server on the server side and JavaScript for the User Interface (UI) Design
CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS Katmoko Ari Sambodo; Aniati Murni; Mahdi Kartasasmita
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1212

Abstract

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia.
Co-Authors Febrian, Andreas Heru Suhartanto Jasni Mohammad Zain Katmoko Ari Sambodo Kusmardi , Mahdi Kartasasmita Toto Haryanto Yusoff, Marina