Laryngeal cancer diagnosis based on improved YOLOv8 algorithm
Laryngeal cancer is the most common malignant tumor in the head and neck region. The larynx, also known as the voice box, plays a crucial role in voice production and ventilation. Enhancing the diagnosis and treatment of laryngeal cancer can significantly improve patients’ prognosis and quality of l...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ada2d9 |
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| author | Xin Nie Xueyan Zhang Di Wang Yuankun Liu Lumin Xing Wenjian Liu |
| author_facet | Xin Nie Xueyan Zhang Di Wang Yuankun Liu Lumin Xing Wenjian Liu |
| author_sort | Xin Nie |
| collection | DOAJ |
| description | Laryngeal cancer is the most common malignant tumor in the head and neck region. The larynx, also known as the voice box, plays a crucial role in voice production and ventilation. Enhancing the diagnosis and treatment of laryngeal cancer can significantly improve patients’ prognosis and quality of life. Artificial intelligence (AI) technology shows promise as a valuable tool for diagnosing laryngeal cancer. It not only reduces the burden on endoscopists in interpreting images but also performs screening and diagnosis efficiently and accurately. However, due to the hidden and diverse nature of laryngeal cancer lesions, achieving accuracy and efficiency in AI-based diagnosis presents poses challenges. This study introduces an improved YOLOv8 algorithm named MSEC-YOLO, specifically designed for the detection and classification tasks of laryngeal cancer in endoscopic images. A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. Additionally, a tiny fully convolutional network architecture has been employed, reducing the number of model parameters and computational costs while maintaining or enhancing performance, which is crucial for real-time medical imaging analysis. The experiments utilized a real-world endoscopic image dataset from the hospital, and the results indicated that MSEC-YOLO outperformed the original YOLOv8 model and its multi-kernel versions across multiple evaluation metrics, especially in critical categories such as malignant tumors, polyps, and papillomas, demonstrating extremely high precision and recall rates. |
| format | Article |
| id | doaj-art-238776379a4a478c9cd84738167b0bad |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-238776379a4a478c9cd84738167b0bad2025-01-21T12:12:30ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101501110.1088/2632-2153/ada2d9Laryngeal cancer diagnosis based on improved YOLOv8 algorithmXin Nie0https://orcid.org/0009-0008-6329-0654Xueyan Zhang1https://orcid.org/0009-0003-2744-4146Di Wang2Yuankun Liu3Lumin Xing4Wenjian Liu5Faculty of Data Science, City University of Macao , Macao S.A.R. 999078, People’s Republic of China; Chongqing Jianzhu College , Chongqing 400000, People’s Republic of ChinaFaculty of Data Science, City University of Macao , Macao S.A.R. 999078, People’s Republic of China; School of Information Work Office, Shandong Youth University of Political Science , Jinan, People’s Republic of ChinaFaculty of Data Science, City University of Macao , Macao S.A.R. 999078, People’s Republic of China; Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) , Jinan 250101, People’s Republic of China; Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science , Jinan 250101, People’s Republic of ChinaFaculty of Data Science, City University of Macao , Macao S.A.R. 999078, People’s Republic of ChinaThe First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital , Jinan, People’s Republic of ChinaFaculty of Data Science, City University of Macao , Macao S.A.R. 999078, People’s Republic of ChinaLaryngeal cancer is the most common malignant tumor in the head and neck region. The larynx, also known as the voice box, plays a crucial role in voice production and ventilation. Enhancing the diagnosis and treatment of laryngeal cancer can significantly improve patients’ prognosis and quality of life. Artificial intelligence (AI) technology shows promise as a valuable tool for diagnosing laryngeal cancer. It not only reduces the burden on endoscopists in interpreting images but also performs screening and diagnosis efficiently and accurately. However, due to the hidden and diverse nature of laryngeal cancer lesions, achieving accuracy and efficiency in AI-based diagnosis presents poses challenges. This study introduces an improved YOLOv8 algorithm named MSEC-YOLO, specifically designed for the detection and classification tasks of laryngeal cancer in endoscopic images. A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. Additionally, a tiny fully convolutional network architecture has been employed, reducing the number of model parameters and computational costs while maintaining or enhancing performance, which is crucial for real-time medical imaging analysis. The experiments utilized a real-world endoscopic image dataset from the hospital, and the results indicated that MSEC-YOLO outperformed the original YOLOv8 model and its multi-kernel versions across multiple evaluation metrics, especially in critical categories such as malignant tumors, polyps, and papillomas, demonstrating extremely high precision and recall rates.https://doi.org/10.1088/2632-2153/ada2d9endoscopiclaryngeal cancermultiscale featureobject detection |
| spellingShingle | Xin Nie Xueyan Zhang Di Wang Yuankun Liu Lumin Xing Wenjian Liu Laryngeal cancer diagnosis based on improved YOLOv8 algorithm Machine Learning: Science and Technology endoscopic laryngeal cancer multiscale feature object detection |
| title | Laryngeal cancer diagnosis based on improved YOLOv8 algorithm |
| title_full | Laryngeal cancer diagnosis based on improved YOLOv8 algorithm |
| title_fullStr | Laryngeal cancer diagnosis based on improved YOLOv8 algorithm |
| title_full_unstemmed | Laryngeal cancer diagnosis based on improved YOLOv8 algorithm |
| title_short | Laryngeal cancer diagnosis based on improved YOLOv8 algorithm |
| title_sort | laryngeal cancer diagnosis based on improved yolov8 algorithm |
| topic | endoscopic laryngeal cancer multiscale feature object detection |
| url | https://doi.org/10.1088/2632-2153/ada2d9 |
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