Effective multi-class lungdisease classification using the hybridfeature engineering mechanism
The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-11-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023896?viewType=HTML |
| _version_ | 1797403745222393856 |
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| author | Binju Saju Neethu Tressa Rajesh Kumar Dhanaraj Sumegh Tharewal Jincy Chundamannil Mathew Danilo Pelusi |
| author_facet | Binju Saju Neethu Tressa Rajesh Kumar Dhanaraj Sumegh Tharewal Jincy Chundamannil Mathew Danilo Pelusi |
| author_sort | Binju Saju |
| collection | DOAJ |
| description | The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring. |
| first_indexed | 2024-03-09T02:42:51Z |
| format | Article |
| id | doaj.art-f939aba6ac8048a88deb57a1fffd7598 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-09T02:42:51Z |
| publishDate | 2023-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-f939aba6ac8048a88deb57a1fffd75982023-12-06T01:17:31ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-11-012011202452027310.3934/mbe.2023896Effective multi-class lungdisease classification using the hybridfeature engineering mechanismBinju Saju0Neethu Tressa1Rajesh Kumar Dhanaraj2Sumegh Tharewal 3Jincy Chundamannil Mathew4Danilo Pelusi51. Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India1. Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India2. Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India2. Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India1. Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India3. Department of Communication Sciences, University of Teramo, Teramo, ItalyThe utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring.https://www.aimspress.com/article/doi/10.3934/mbe.2023896?viewType=HTMLchest x-raylung diseaseotsucontrast limited adaptive histogram equalizationcanny edge detectiondensenet121batch equalizationaquila optimizer |
| spellingShingle | Binju Saju Neethu Tressa Rajesh Kumar Dhanaraj Sumegh Tharewal Jincy Chundamannil Mathew Danilo Pelusi Effective multi-class lungdisease classification using the hybridfeature engineering mechanism Mathematical Biosciences and Engineering chest x-ray lung disease otsu contrast limited adaptive histogram equalization canny edge detection densenet121 batch equalization aquila optimizer |
| title | Effective multi-class lungdisease classification using the hybridfeature engineering mechanism |
| title_full | Effective multi-class lungdisease classification using the hybridfeature engineering mechanism |
| title_fullStr | Effective multi-class lungdisease classification using the hybridfeature engineering mechanism |
| title_full_unstemmed | Effective multi-class lungdisease classification using the hybridfeature engineering mechanism |
| title_short | Effective multi-class lungdisease classification using the hybridfeature engineering mechanism |
| title_sort | effective multi class lungdisease classification using the hybridfeature engineering mechanism |
| topic | chest x-ray lung disease otsu contrast limited adaptive histogram equalization canny edge detection densenet121 batch equalization aquila optimizer |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023896?viewType=HTML |
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