AI Based Covid Pneumonia Classifier using Machine Learning

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Published Oct 8, 2021
A Sowmiya C Shilaja G Nalinashini N Padmavathi Mayakannan Selvaraju

Abstract

This paper offers a convolutional neural network model trained from scratch to categorize and detect the presence of pneumonia or COVID 19 in a set of chest X-ray pictures. To achieve a remarkable classification performance, other methods rely on transfer learning approaches or traditional handcrafted techniques. However, in our work, we built a convolutional neural network model to extract features from a given chest X-ray image and classify it to determine if a person is affected with pneumonia or COVID 19. This approach could be able to help with the dependability and interpretability issues that come up frequently when dealing with medical images. It is difficult to obtain a large amount of pneumonia dataset for this classification task, as it is for other deep learning classification tasks with sufficient image repositories. As a result, we used several data augmentation algorithms to improve the CNN model's validation and classification accuracy, and we achieved remarkable validation accuracy.

How to Cite

A Sowmiya, C Shilaja, G Nalinashini, N Padmavathi, & Selvaraju, M. (2021). AI Based Covid Pneumonia Classifier using Machine Learning. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/1867
Abstract 62 |

Article Details

Keywords

Convolutional neural network, deep neural network, artificial neural network, graphical user interface.

References
[1] Sultana, Sufian, Dutta, “Advancements in Image Classification using Convolutional Neural Network”, 2019.
[2] Zhao, Zheng, Xu, Wu, “Object Detection with Deep Learning: A Review”, 2019.
[3] Liu, Deng, Yang, “Recent progress in semantic image segmentation”, 2018.
[4] Ganesan, Subashini, “Classification of medical X-ray images for automated annotation”, 2014.
[5] Zare, Mueen, Awedh, Seng, “Automatic classification of medical X-ray images with convolutional neural network: hybrid generative-discriminative approach”, 2013.
Section
GE3- Computers & Information Technology

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