Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach
Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/2/260 |
| _version_ | 1797443834733395968 |
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| author | Yaman Akbulut |
| author_facet | Yaman Akbulut |
| author_sort | Yaman Akbulut |
| collection | DOAJ |
| description | Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90–10%, 80–20%, and 70–30%). For 90–10%, 80–20%, and 70–30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets. |
| first_indexed | 2024-03-09T13:02:47Z |
| format | Article |
| id | doaj.art-6c89d93af2d7452eb15d046cce4e5a9d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T13:02:47Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-6c89d93af2d7452eb15d046cce4e5a9d2023-11-30T21:52:15ZengMDPI AGDiagnostics2075-44182023-01-0113226010.3390/diagnostics13020260Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning ApproachYaman Akbulut0Department of Software Engineering, Faculty of Technology, Firat University, Elazig 23200, TurkeyMany people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90–10%, 80–20%, and 70–30%). For 90–10%, 80–20%, and 70–30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets.https://www.mdpi.com/2075-4418/13/2/260ILDMCW segmentationcustomized deep learning |
| spellingShingle | Yaman Akbulut Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach Diagnostics ILD MCW segmentation customized deep learning |
| title | Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach |
| title_full | Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach |
| title_fullStr | Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach |
| title_full_unstemmed | Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach |
| title_short | Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach |
| title_sort | automated pneumonia based lung diseases classification with robust technique based on a customized deep learning approach |
| topic | ILD MCW segmentation customized deep learning |
| url | https://www.mdpi.com/2075-4418/13/2/260 |
| work_keys_str_mv | AT yamanakbulut automatedpneumoniabasedlungdiseasesclassificationwithrobusttechniquebasedonacustomizeddeeplearningapproach |