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| Paper Title: | Comparative Analysis and Validation for Diagnosis of Pneumonia through Chest X-rays using Deep Learning Models |
| Authors Name: | Yashashree Shinde , Shivani Pandey , Shatabdi Pingale , Sakshi Rathi , Sonali Surpatne |
| Unique Id: | IJSDR2305130 |
| Published In: | Volume 8 Issue 5, May-2023 |
| Abstract: | Pneumonia has been the widespread disease caused by a respiratory infection and has rapid spread and relatively high mortality rate. It is mostly found in the children of age group of five. Early detection and treatment of pneumonia will significantly reduce its mortality rate. X-ray diagnosis is currently recognized as a relatively effective method to diagnose the pneumonia. An experienced doctor's visual analysis of a patient's X-ray chest radiograph takes about 5 to 15 minutes. When cases are concentrated, the doctor's clinical diagnosis is undoubtedly put under tremendous strain. As a result, relying on the imaging doctor's naked eye has a very low efficiency. So, it is important to use AI to help doctors diagnose pneumonia from clinical images. Furthermore, artificial intelligence recognition is extremely fast, and convolutional neural networks (CNNs) have outperformed humans in image identification. To achieve a solution to the problem we used the Kaggle dataset with chest X-ray images for classification, which included 5216 train and 624 test images and two classes: normal and pneumonia. We conducted studies in which we used five mainstream network algorithms to classify these diseases in the dataset and compared the results, in which custom CNN model achieved a higher accuracy rate than other methods. The accuracy gained was about 92.7 % Additionally, the improved Custom CNN network may be extended to other areas for application and better results. |
| Keywords: | Pneumonia, Custom CNN, Resent-50, VGG-16, Dense Net, Inception |
| Cite Article: | "Comparative Analysis and Validation for Diagnosis of Pneumonia through Chest X-rays using Deep Learning Models", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.866 - 871, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305130.pdf |
| Downloads: | 000223171 |
| Publication Details: | Published Paper ID: IJSDR2305130 Registration ID:206204 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: 866 - 871 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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