Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases: Difference between revisions

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== Previous Work ==  
== Previous Work ==  
Throughout the last decade, scientists have been trying to integrate the application of AI, ML, DL in healthcare system. Researchers have utilized CNN to solve challenges in medicine such as disease detection using classification and segmentation approaches in skin disorders, brain and breast cancer, and in diabetes (retinopathy) diseases. In the field of microbiology, microbiologists, radiologists and computer scientists have been working together to detect microbial diseases such as tuberculosis, malaria and pneumonia using computer aided diagnosis.
Throughout the last decade, scientists have been trying to integrate the application of AI, ML, DL in healthcare system. Researchers have utilized CNN to solve challenges in medicine such as disease detection using classification and segmentation approaches in skin disorders, brain and breast cancer, and in diabetes (retinopathy) diseases. In the field of microbiology, microbiologists, radiologists and computer scientists have been working together to detect microbial diseases such as tuberculosis, malaria and pneumonia using computer aided diagnosis.
X-ray images are the basic data used for detection of pneumonia using ML approach. This idea is adopted by Stephen et al. (2019). The authors utilized DL approach to classify X-ray images samples. The research employed a CNN that is built from scratch using Keras open source with TensorFlow to extract distinctive features from positive and negative images. The dataset contains 5856 X-ray images of normal and pneumonia images collected from pediatric patients between 1 to 5 years old. The dataset was further augmented to yield a greater number of training dataset. The model was tested on different data size (100–300) and the model achieved average accuracy of 94.81%, 93.01% training and validation respectively.
X-ray images are the basic data used for detection of pneumonia using ML approach. This idea is adopted by Stephen et al. (2019). The authors utilized DL approach to classify X-ray images samples. The research employed a CNN that is built from scratch using Keras open source with TensorFlow to extract distinctive features from positive and negative images. The dataset contains 5856 X-ray images of normal and pneumonia images collected from pediatric patients between 1 to 5 years old. The dataset was further augmented to yield a greater number of training dataset. The model was tested on different data size (100–300) and the model achieved average accuracy of 94.81%, 93.01% training and validation respectively.

Revision as of 14:16, 21 November 2021

Presented by

Kun Wang

Introduction

In this paper, the authors utilized Pre-trained (through transfer learning) AlexNet model for detection of pneumonia in CT Scan images. Then they carried out 10 k cross validation to estimate the model will perform on unseen dataset. And finally they evaluated the performance of the models based on accuracy, sensitivity and specificity for general dataset and mean average of the parameters for 10 K cross validation.

Previous Work

Throughout the last decade, scientists have been trying to integrate the application of AI, ML, DL in healthcare system. Researchers have utilized CNN to solve challenges in medicine such as disease detection using classification and segmentation approaches in skin disorders, brain and breast cancer, and in diabetes (retinopathy) diseases. In the field of microbiology, microbiologists, radiologists and computer scientists have been working together to detect microbial diseases such as tuberculosis, malaria and pneumonia using computer aided diagnosis.

X-ray images are the basic data used for detection of pneumonia using ML approach. This idea is adopted by Stephen et al. (2019). The authors utilized DL approach to classify X-ray images samples. The research employed a CNN that is built from scratch using Keras open source with TensorFlow to extract distinctive features from positive and negative images. The dataset contains 5856 X-ray images of normal and pneumonia images collected from pediatric patients between 1 to 5 years old. The dataset was further augmented to yield a greater number of training dataset. The model was tested on different data size (100–300) and the model achieved average accuracy of 94.81%, 93.01% training and validation respectively.