An Efficient Detection of Pneumonia by Deep Learning Techniques
| Autor: | N.T. Renukadevi, N. Vigneshwaran, S. Nithin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition medicine.disease Convolutional neural network Support vector machine Identification (information) Pneumonia ComputingMethodologies_PATTERNRECOGNITION Metric (mathematics) medicine Artificial intelligence business |
| Zdroj: | 2021 2nd Global Conference for Advancement in Technology (GCAT). |
| DOI: | 10.1109/gcat52182.2021.9587820 |
| Popis: | A disease which is caused by bacterial infections in lungs is Pneumonia. Identification of this disease in an initial stage is very important. Usually, it can be detected using chest X-ray images by medical practitioners. Sometimes, to identify the presence of disease computer-aided diagnosis is needed. In this work, the popular machine learning algorithm Support Vector Machine (SVM) and deep neural network such as Convolutional Neural Networks (CNN) are being used for the detection of pneumonia. Both SVM and CNN have their own technical benefits in classifying the medical images. Here, the performances metric such as classification accuracy of both these algorithms were evaluated and compared. |
| Databáze: | OpenAIRE |
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