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Published in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019
The paper outlines a deep learning method to classify brain tumors from MRI images using a CNN model. The model achieves high accuracy and precision by preprocessing images and applying advanced neural network techniques, significantly aiding in the diagnostic process of identifying different types of brain tumors.
Recommended citation: S. Das, O. F. M. R. R. Aranya and N. N. Labiba, "Brain Tumor Classification Using Convolutional Neural Network," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-5, doi: 10.1109/ICASERT.2019.8934603.
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Published in Informatics in Medicine Unlocked, Volume 26, 2021
The paper presents COV-VGX, an automated detection system for COVID-19 using chest X-ray images through deep learning and transfer learning approaches. It employs two classifiers: a multiclass classifier to discern among COVID-19, pneumonia, and normal cases, and a binary classifier for COVID-19 and pneumonia. Leveraging the VGG-16 model, it achieves significant accuracy and precision, demonstrating its potential as an effective tool in diagnosing COVID-19 in a clinical setting. The paper also discusses the challenges of limited COVID-19 image datasets and the importance of accurate testing to prevent disease spread.
Recommended citation: P. Saha, M. Sadi, O. Aranya, S. Jahan and F. Islam, "COV-VGX: An automated COVID-19 detection system using X -ray images and transfer learning", Informatics in Medicine Unlocked, vol. 26, pp. 100741, 2021.
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CS-2233, Department of Computer Science, UTSA, 2023
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CS-3443, Department of Computer Science, UTSA, 2024
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CS-3853, Department of Computer Science, UTSA, 2024
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