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2021 | OriginalPaper | Buchkapitel

3. A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence

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Abstract

Image retrieval and classification are the most prominent area of research in computer vision. Nowadays, bounteous medical images are generated through different types of medical imaging modalities in healthcare systems. It is often very difficult for researchers and doctors to access manage and retrieve images easily. The efficient and effective analysis and usage of heterogeneous biomedical images growing rapidly are a tedious task. Content-based image retrieval (CBIR) is one of the most widely used methods for automatic retrieval of images and widely used in medical images. Abundant research articles are published in different domain of applications related to CBIR and classification. The aim of this study is to provide a road map for researchers by exploring the various approaches, techniques, and algorithms used for medical image retrieval and classification. Feature extraction is the main subject for improving the performance of image classification and retrieval. Bag of visual words techniques and deep convolutional neural networks are widely used in content-based medical image retrieval (CBMIR). The state-of-the-art methods presented in this review are well suited to classify and retrieve multimodal medical images for different body organs. The methods include preprocessing of images, feature extraction, classification, and retrieval steps to develop an efficient biomedical image retrieval system. This chapter briefly reviews the various techniques used for biomedical images, and different methods adopted in classification and retrieval are focused.

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Literatur
1.
Zurück zum Zitat Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020). Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters, 135, 346–353.CrossRef Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020). Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters, 135, 346–353.CrossRef
2.
Zurück zum Zitat Owais, M., Arsalan, M., Choi, J., & Park, K. R. (2019). Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. Journal of Clinical Medicine, 8(4), 462.CrossRef Owais, M., Arsalan, M., Choi, J., & Park, K. R. (2019). Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. Journal of Clinical Medicine, 8(4), 462.CrossRef
3.
Zurück zum Zitat Carvalho, E. D., Antônio Filho, O. C., Silva, R. R., Araújo, F. H., Diniz, J. O., Silva, A. C., … Gattass, M. (2020). Breast cancer diagnosis from histopathological images using textural features and CBIR. Artificial Intelligence in Medicine, 105, 101845.CrossRef Carvalho, E. D., Antônio Filho, O. C., Silva, R. R., Araújo, F. H., Diniz, J. O., Silva, A. C., … Gattass, M. (2020). Breast cancer diagnosis from histopathological images using textural features and CBIR. Artificial Intelligence in Medicine, 105, 101845.CrossRef
4.
Zurück zum Zitat Wong, K. K., Fortino, G., & Abbott, D. (2020). Deep learning-based cardiovascular image diagnosis: A promising challenge. Future Generation Computer Systems, 110, 802–811.CrossRef Wong, K. K., Fortino, G., & Abbott, D. (2020). Deep learning-based cardiovascular image diagnosis: A promising challenge. Future Generation Computer Systems, 110, 802–811.CrossRef
5.
Zurück zum Zitat Haripriya, P., & Porkodi, R. (2021). Parallel deep convolutional neural network for content based medical image retrieval. Journal of Ambient Intelligence and Humanized. Haripriya, P., & Porkodi, R. (2021). Parallel deep convolutional neural network for content based medical image retrieval. Journal of Ambient Intelligence and Humanized.
6.
Zurück zum Zitat Haq, N. F., Moradi, M., & Wang, Z. J. (2020). A deep community based approach for large scale content based X-ray image retrieval. Medical Image Analysis, 68, 101847.CrossRef Haq, N. F., Moradi, M., & Wang, Z. J. (2020). A deep community based approach for large scale content based X-ray image retrieval. Medical Image Analysis, 68, 101847.CrossRef
7.
Zurück zum Zitat Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2020). CNN-based health model for regular health factors analysis in internet-of-medical things environment. IEEE Access, 8, 52541–52549.CrossRef Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2020). CNN-based health model for regular health factors analysis in internet-of-medical things environment. IEEE Access, 8, 52541–52549.CrossRef
8.
Zurück zum Zitat Karimi, D., Dou, H., Warfield, S. K., & Gholipour, A. (2020). Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Medical Image Analysis, 65, 101759.CrossRef Karimi, D., Dou, H., Warfield, S. K., & Gholipour, A. (2020). Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Medical Image Analysis, 65, 101759.CrossRef
9.
Zurück zum Zitat Kaur, P., & Singh, R. K. (2020, June). A panoramic view of content-based medical image retrieval system. In 2020 international conference on intelligent engineering and management (ICIEM) (pp. 187–192). IEEE. Kaur, P., & Singh, R. K. (2020, June). A panoramic view of content-based medical image retrieval system. In 2020 international conference on intelligent engineering and management (ICIEM) (pp. 187–192). IEEE.
10.
Zurück zum Zitat Khan, S. R., Sikandar, M., Almogren, A., Din, I. U., Guerrieri, A., & Fortino, G. (2020). IoMT-based computational approach for detecting brain tumor. Future Generation Computer Systems, 109, 360–367.CrossRef Khan, S. R., Sikandar, M., Almogren, A., Din, I. U., Guerrieri, A., & Fortino, G. (2020). IoMT-based computational approach for detecting brain tumor. Future Generation Computer Systems, 109, 360–367.CrossRef
11.
Zurück zum Zitat Kumar, A., Kim, J., Cai, W., Fulham, M., & Feng, D. (2013). Content-based medical image retrieval: A survey of applications to multidimensional and multimodality data. Journal of Digital Imaging, 26(6), 1025–1039.CrossRef Kumar, A., Kim, J., Cai, W., Fulham, M., & Feng, D. (2013). Content-based medical image retrieval: A survey of applications to multidimensional and multimodality data. Journal of Digital Imaging, 26(6), 1025–1039.CrossRef
12.
Zurück zum Zitat Kumar, M., & Singh, M. (2016). CBMIR: Content based medical image retrieval system using texture and intensity for eye images. Kumar, M., & Singh, M. (2016). CBMIR: Content based medical image retrieval system using texture and intensity for eye images.
13.
Zurück zum Zitat Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.CrossRef Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.CrossRef
14.
Zurück zum Zitat Lowe, D. G. (1999, September). Object recognition from local scale-invariant features. In proceedings of the seventh IEEE international conference on computer vision (Vol. 2, pp. 1150–1157). IEEE. Lowe, D. G. (1999, September). Object recognition from local scale-invariant features. In proceedings of the seventh IEEE international conference on computer vision (Vol. 2, pp. 1150–1157). IEEE.
15.
Zurück zum Zitat Muhammad, K., Khan, S., Del Ser, J., & de Albuquerque, V. H. C. (2020). Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE transactions on neural networks and learning systems. Muhammad, K., Khan, S., Del Ser, J., & de Albuquerque, V. H. C. (2020). Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE transactions on neural networks and learning systems.
16.
Zurück zum Zitat Nandpuru, H. B., Salankar, S. S., & Bora, V. R. (2014, March). MRI brain cancer classification using support vector machine. In 2014 IEEE Students’ conference on electrical, electronics and computer science (pp. 1–6). IEEE. Nandpuru, H. B., Salankar, S. S., & Bora, V. R. (2014, March). MRI brain cancer classification using support vector machine. In 2014 IEEE Students’ conference on electrical, electronics and computer science (pp. 1–6). IEEE.
17.
Zurück zum Zitat Piccialli, F., Di Somma, V., Giampaolo, F., Cuomo, S., & Fortino, G. (2021). A survey on deep learning in medicine: Why, how and when? Information Fusion, 66, 111–137.CrossRef Piccialli, F., Di Somma, V., Giampaolo, F., Cuomo, S., & Fortino, G. (2021). A survey on deep learning in medicine: Why, how and when? Information Fusion, 66, 111–137.CrossRef
18.
Zurück zum Zitat Pilevar, A. H. (2011). CBMIR: Content-based image retrieval algorithm for medical image databases. Journal of Medical Signals and Sensors, 1(1), 12.CrossRef Pilevar, A. H. (2011). CBMIR: Content-based image retrieval algorithm for medical image databases. Journal of Medical Signals and Sensors, 1(1), 12.CrossRef
19.
Zurück zum Zitat Greenspan, H., & Pinhas, A. T. (2007). Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Transactions on Information Technology in Biomedicine, 11(2), 190–202.CrossRef Greenspan, H., & Pinhas, A. T. (2007). Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Transactions on Information Technology in Biomedicine, 11(2), 190–202.CrossRef
20.
Zurück zum Zitat Alinsaif, S., & Lang, J. (2020). Texture features in the Shearlet domain for histopathological image classification. BMC Medical Informatics and Decision Making, 20(14), 1–19. Alinsaif, S., & Lang, J. (2020). Texture features in the Shearlet domain for histopathological image classification. BMC Medical Informatics and Decision Making, 20(14), 1–19.
21.
Zurück zum Zitat Alroobaea, R., Rubaiee, S., Bourouis, S., Bouguila, N., & Alsufyani, A. (2020). Bayesian inference framework for bounded generalized Gaussian-based mixture model and its application to biomedical images classification. International Journal of Imaging Systems and Technology, 30(1), 18–30.CrossRef Alroobaea, R., Rubaiee, S., Bourouis, S., Bouguila, N., & Alsufyani, A. (2020). Bayesian inference framework for bounded generalized Gaussian-based mixture model and its application to biomedical images classification. International Journal of Imaging Systems and Technology, 30(1), 18–30.CrossRef
22.
Zurück zum Zitat Asnaoui, K. E., Chawki, Y., & Idri, A. (2020). Automated methods for detection and classification pneumonia based on x-ray images using deep learning. arXiv preprint arXiv:2003.14363. Asnaoui, K. E., Chawki, Y., & Idri, A. (2020). Automated methods for detection and classification pneumonia based on x-ray images using deep learning. arXiv preprint arXiv:2003.14363.
23.
Zurück zum Zitat Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., … van Ginneken, B. (2015). Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical Image Analysis, 26(1), 195–202.CrossRef Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., … van Ginneken, B. (2015). Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical Image Analysis, 26(1), 195–202.CrossRef
24.
Zurück zum Zitat Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., … van Ginneken, B. (2016). Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5), 1160–1169.CrossRef Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., … van Ginneken, B. (2016). Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5), 1160–1169.CrossRef
25.
Zurück zum Zitat van Tulder, G., & de Bruijne, M. (2016). Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Transactions on Medical Imaging, 35(5), 1262–1272.CrossRef van Tulder, G., & de Bruijne, M. (2016). Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Transactions on Medical Imaging, 35(5), 1262–1272.CrossRef
26.
Zurück zum Zitat Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 35(5), 1207–1216.CrossRef Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 35(5), 1207–1216.CrossRef
27.
Zurück zum Zitat Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., … Zhou, X. S. (2016). Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE Transactions on Medical Imaging, 35(5), 1332–1343.CrossRef Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., … Zhou, X. S. (2016). Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE Transactions on Medical Imaging, 35(5), 1332–1343.CrossRef
28.
Zurück zum Zitat Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., … Heng, P. A. (2016). Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Transactions on Medical Imaging, 35(5), 1182–1195.CrossRef Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., … Heng, P. A. (2016). Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Transactions on Medical Imaging, 35(5), 1182–1195.CrossRef
29.
Zurück zum Zitat Chowdhury, M., Bulo, S. R., Moreno, R., Kundu, M. K., & Smedby, Ö. (2016, December). An efficient radiographic image retrieval system using convolutional neural network. In 2016 23rd international conference on pattern recognition (ICPR) (pp. 3134–3139). IEEE. Chowdhury, M., Bulo, S. R., Moreno, R., Kundu, M. K., & Smedby, Ö. (2016, December). An efficient radiographic image retrieval system using convolutional neural network. In 2016 23rd international conference on pattern recognition (ICPR) (pp. 3134–3139). IEEE.
30.
Zurück zum Zitat Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017). Medical image retrieval using deep convolutional neural network. Neurocomputing, 266, 8–20.CrossRef Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017). Medical image retrieval using deep convolutional neural network. Neurocomputing, 266, 8–20.CrossRef
31.
Zurück zum Zitat Scherer, R., & Ditzinger, S. (2020). Computer vision methods for fast image classification and retrieval. Springer International Publishing.CrossRef Scherer, R., & Ditzinger, S. (2020). Computer vision methods for fast image classification and retrieval. Springer International Publishing.CrossRef
32.
Zurück zum Zitat Amini, A., Chen, W., Fortino, G., Li, Y., Pan, Y., & Wang, M. D. (2020). Editorial special issue on “AI-driven informatics, sensing, imaging and big data analytics for fighting the COVID-19 pandemic”. IEEE Journal of Biomedical and Health Informatics, 24(10), 2731–2732.CrossRef Amini, A., Chen, W., Fortino, G., Li, Y., Pan, Y., & Wang, M. D. (2020). Editorial special issue on “AI-driven informatics, sensing, imaging and big data analytics for fighting the COVID-19 pandemic”. IEEE Journal of Biomedical and Health Informatics, 24(10), 2731–2732.CrossRef
33.
Zurück zum Zitat Ahmed, A. (2020). Implementing relevance feedback for content-based medical image retrieval. IEEE Access, 8, 79969–79976.CrossRef Ahmed, A. (2020). Implementing relevance feedback for content-based medical image retrieval. IEEE Access, 8, 79969–79976.CrossRef
34.
Zurück zum Zitat Swapna, T., & Kunnan, S. Content-based image retrieval system for bio-medical images. Swapna, T., & Kunnan, S. Content-based image retrieval system for bio-medical images.
35.
Zurück zum Zitat Behnam, M., & Pourghassem, H. (2013, December). Feature descriptor optimization in medical image retrieval based on genetic algorithm. In 2013 20th Iranian conference on biomedical engineering (ICBME) (pp. 280–285). IEEE. Behnam, M., & Pourghassem, H. (2013, December). Feature descriptor optimization in medical image retrieval based on genetic algorithm. In 2013 20th Iranian conference on biomedical engineering (ICBME) (pp. 280–285). IEEE.
36.
Zurück zum Zitat Camalan, S., Niazi, M. K. K., Moberly, A. C., Teknos, T., Essig, G., Elmaraghy, C., … Gurcan, M. N. (2020). OtoMatch: Content-based eardrum image retrieval using deep learning. PLoS One, 15(5), e0232776.CrossRef Camalan, S., Niazi, M. K. K., Moberly, A. C., Teknos, T., Essig, G., Elmaraghy, C., … Gurcan, M. N. (2020). OtoMatch: Content-based eardrum image retrieval using deep learning. PLoS One, 15(5), e0232776.CrossRef
37.
Zurück zum Zitat Fallahi, A. R., Pooyan, M., & Mohammadnejad, H. (2009, June). Application of morphological operations in human brain CT image with SVM. In 2009 3rd international conference on bioinformatics and biomedical engineering (pp. 1–4). IEEE. Fallahi, A. R., Pooyan, M., & Mohammadnejad, H. (2009, June). Application of morphological operations in human brain CT image with SVM. In 2009 3rd international conference on bioinformatics and biomedical engineering (pp. 1–4). IEEE.
38.
Zurück zum Zitat Garg, M., & Dhiman, G. (2021). A novel content based image retrieval approach for classification using glcm features and texture fused lbp variants. Neural Computing and Applications (Vol. 33, pp. 1311–1328). Garg, M., & Dhiman, G. (2021). A novel content based image retrieval approach for classification using glcm features and texture fused lbp variants. Neural Computing and Applications (Vol. 33, pp. 1311–1328).
39.
Zurück zum Zitat Quelhas, P., Monay, F., Odobez, J. M., Gatica-Perez, D., & Tuytelaars, T. (2007). A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1575–1589.CrossRef Quelhas, P., Monay, F., Odobez, J. M., Gatica-Perez, D., & Tuytelaars, T. (2007). A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1575–1589.CrossRef
40.
Zurück zum Zitat Nithya, S., & ShineLet, G. (2012). Bio-medical image retrieval using SVM. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), 14–18. Nithya, S., & ShineLet, G. (2012). Bio-medical image retrieval using SVM. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(10), 14–18.
41.
Zurück zum Zitat Alhindi, T. J., Kalra, S., Ng, K. H., Afrin, A., & Tizhoosh, H. R. (2018, July). Comparing LBP, HOG and deep features for classification of histopathology images. In 2018 international joint conference on neural networks (IJCNN) (pp. 1–7). IEEE. Alhindi, T. J., Kalra, S., Ng, K. H., Afrin, A., & Tizhoosh, H. R. (2018, July). Comparing LBP, HOG and deep features for classification of histopathology images. In 2018 international joint conference on neural networks (IJCNN) (pp. 1–7). IEEE.
42.
Zurück zum Zitat Greeshma, K. V., & Sreekumar, K. (2019). Hyperparameter optimization and regularization on fashion-MNIST classification. Greeshma, K. V., & Sreekumar, K. (2019). Hyperparameter optimization and regularization on fashion-MNIST classification.
43.
Zurück zum Zitat Bansal, D., Khanna, K., Chhikara, R., Dua, R. K., & Malhotra, R. (2020). Classification of magnetic resonance images using bag of features for detecting dementia. Procedia Computer Science, 167, 131–137.CrossRef Bansal, D., Khanna, K., Chhikara, R., Dua, R. K., & Malhotra, R. (2020). Classification of magnetic resonance images using bag of features for detecting dementia. Procedia Computer Science, 167, 131–137.CrossRef
44.
Zurück zum Zitat Greeshma, K. V., & Gripsy, J. V. (2020). Image classification using HOG and LBP feature descriptors with SVM and CNN. Greeshma, K. V., & Gripsy, J. V. (2020). Image classification using HOG and LBP feature descriptors with SVM and CNN.
45.
Zurück zum Zitat Othman, M. F. B., Abdullah, N. B., & Kamal, N. F. B. (2011, April). MRI brain classification using support vector machine. In 2011 fourth international conference on modeling, simulation and applied optimization (pp. 1–4). IEEE. Othman, M. F. B., Abdullah, N. B., & Kamal, N. F. B. (2011, April). MRI brain classification using support vector machine. In 2011 fourth international conference on modeling, simulation and applied optimization (pp. 1–4). IEEE.
46.
Zurück zum Zitat Wang, L. (Ed.). (2005). Support vector machines: Theory and applications (Vol. 177). Springer Science & Business Media.MATH Wang, L. (Ed.). (2005). Support vector machines: Theory and applications (Vol. 177). Springer Science & Business Media.MATH
47.
Zurück zum Zitat Wang, Z., Wu, D., Gravina, R., Fortino, G., Jiang, Y., & Tang, K. (2017). Kernel fusion based extreme learning machine for cross-location activity recognition. Information Fusion, 37, 1–9.CrossRef Wang, Z., Wu, D., Gravina, R., Fortino, G., Jiang, Y., & Tang, K. (2017). Kernel fusion based extreme learning machine for cross-location activity recognition. Information Fusion, 37, 1–9.CrossRef
48.
Zurück zum Zitat Alfanindya, A., Hashim, N., & Eswaran, C. (2013, June). Content based image retrieval and classification using speeded-up robust features (SURF) and grouped bag-of-visual-words (GBoVW). In 2013 international conference on technology, informatics, management, engineering and environment (pp. 77–82). IEEE. Alfanindya, A., Hashim, N., & Eswaran, C. (2013, June). Content based image retrieval and classification using speeded-up robust features (SURF) and grouped bag-of-visual-words (GBoVW). In 2013 international conference on technology, informatics, management, engineering and environment (pp. 77–82). IEEE.
49.
Zurück zum Zitat Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404–417). Springer. Bay, H., Tuytelaars, T., & Van Gool, L. (2006, May). Surf: Speeded up robust features. In European conference on computer vision (pp. 404–417). Springer.
50.
Zurück zum Zitat Govindaraju, S., & Kumar, G. P. R. (2016). A novel content based medical image retrieval using SURF features. Indian Journal of Science and Technology, 9(20), 1–8.CrossRef Govindaraju, S., & Kumar, G. P. R. (2016). A novel content based medical image retrieval using SURF features. Indian Journal of Science and Technology, 9(20), 1–8.CrossRef
Metadaten
Titel
A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence
verfasst von
K. V. Greeshma
J. Viji Gripsy
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-75220-0_3

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