A review of intelligent diagnosis methods of imaging gland cancer based on machine learning
Background: Gland cancer is a high-incidence disease endangering human health, and its early detection and treatment need efficient, accurate and objective intelligent diagnosis methods. In recent years, the advent of machine learning techniques has yielded satisfactory results in the intelligent gl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-08-01
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| Series: | Virtual Reality & Intelligent Hardware |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096579622000985 |
| _version_ | 1797737382969081856 |
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| author | Han Jiang Wen-Jia Sun Han-Fei Guo Jia-Yuan Zeng Xin Xue Shuai Li |
| author_facet | Han Jiang Wen-Jia Sun Han-Fei Guo Jia-Yuan Zeng Xin Xue Shuai Li |
| author_sort | Han Jiang |
| collection | DOAJ |
| description | Background: Gland cancer is a high-incidence disease endangering human health, and its early detection and treatment need efficient, accurate and objective intelligent diagnosis methods. In recent years, the advent of machine learning techniques has yielded satisfactory results in the intelligent gland cancer diagnosis based on clinical images, greatly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors. The foci of this paper is to review, classify and analyze the intelligent diagnosis methods of imaging gland cancer based on machine learning and deep learning. To start with, the paper presents a brief introduction about some basic imaging principles of multi-modal medical images, such as the commonly used CT, MRI, US, PET, and pathology. In addition, the intelligent diagnosis methods of imaging gland cancer are further classified into supervised learning and weakly-supervised learning. Supervised learning consists of traditional machine learning methods like KNN, SVM, multilayer perceptron, etc. and deep learning methods evolving from CNN, meanwhile, weakly-supervised learning can be further categorized into active learning, semi-supervised learning and transfer learning. The state-of-the-art methods are illustrated with implementation details, including image segmentation, feature extraction, the optimization of classifiers, and their performances are evaluated through indicators like accuracy, precision and sensitivity. To conclude, the challenges and development trend of intelligent diagnosis methods of imaging gland cancer are addressed and discussed. |
| first_indexed | 2024-03-12T13:27:54Z |
| format | Article |
| id | doaj.art-da7703ea71d04d70b562b395bc796854 |
| institution | Directory Open Access Journal |
| issn | 2096-5796 |
| language | English |
| last_indexed | 2024-03-12T13:27:54Z |
| publishDate | 2023-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Virtual Reality & Intelligent Hardware |
| spelling | doaj.art-da7703ea71d04d70b562b395bc7968542023-08-25T04:24:10ZengKeAi Communications Co., Ltd.Virtual Reality & Intelligent Hardware2096-57962023-08-0154293316A review of intelligent diagnosis methods of imaging gland cancer based on machine learningHan Jiang0Wen-Jia Sun1Han-Fei Guo2Jia-Yuan Zeng3Xin Xue4Shuai Li5Corresponding author.; School of computer science, Beijing University of Aeronautics and Astronautics, Beijing, ChinaSchool of computer science, Beijing University of Aeronautics and Astronautics, Beijing, ChinaSchool of computer science, Beijing University of Aeronautics and Astronautics, Beijing, ChinaSchool of computer science, Beijing University of Aeronautics and Astronautics, Beijing, ChinaSchool of computer science, Beijing University of Aeronautics and Astronautics, Beijing, ChinaSchool of computer science, Beijing University of Aeronautics and Astronautics, Beijing, ChinaBackground: Gland cancer is a high-incidence disease endangering human health, and its early detection and treatment need efficient, accurate and objective intelligent diagnosis methods. In recent years, the advent of machine learning techniques has yielded satisfactory results in the intelligent gland cancer diagnosis based on clinical images, greatly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors. The foci of this paper is to review, classify and analyze the intelligent diagnosis methods of imaging gland cancer based on machine learning and deep learning. To start with, the paper presents a brief introduction about some basic imaging principles of multi-modal medical images, such as the commonly used CT, MRI, US, PET, and pathology. In addition, the intelligent diagnosis methods of imaging gland cancer are further classified into supervised learning and weakly-supervised learning. Supervised learning consists of traditional machine learning methods like KNN, SVM, multilayer perceptron, etc. and deep learning methods evolving from CNN, meanwhile, weakly-supervised learning can be further categorized into active learning, semi-supervised learning and transfer learning. The state-of-the-art methods are illustrated with implementation details, including image segmentation, feature extraction, the optimization of classifiers, and their performances are evaluated through indicators like accuracy, precision and sensitivity. To conclude, the challenges and development trend of intelligent diagnosis methods of imaging gland cancer are addressed and discussed.http://www.sciencedirect.com/science/article/pii/S2096579622000985Gland cancerIntelligent diagnosisMachine learningDeep learningMulti-modal medical images |
| spellingShingle | Han Jiang Wen-Jia Sun Han-Fei Guo Jia-Yuan Zeng Xin Xue Shuai Li A review of intelligent diagnosis methods of imaging gland cancer based on machine learning Virtual Reality & Intelligent Hardware Gland cancer Intelligent diagnosis Machine learning Deep learning Multi-modal medical images |
| title | A review of intelligent diagnosis methods of imaging gland cancer based on machine learning |
| title_full | A review of intelligent diagnosis methods of imaging gland cancer based on machine learning |
| title_fullStr | A review of intelligent diagnosis methods of imaging gland cancer based on machine learning |
| title_full_unstemmed | A review of intelligent diagnosis methods of imaging gland cancer based on machine learning |
| title_short | A review of intelligent diagnosis methods of imaging gland cancer based on machine learning |
| title_sort | review of intelligent diagnosis methods of imaging gland cancer based on machine learning |
| topic | Gland cancer Intelligent diagnosis Machine learning Deep learning Multi-modal medical images |
| url | http://www.sciencedirect.com/science/article/pii/S2096579622000985 |
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