The cancer detection requires integrated precise feature extraction assessed by trained artificial neural network(ANN), in conjunction with the k nearest neighbor method to detect and properly classify MRI cancer image.
Dear Eduard,
You may refer to the following papers:
1. El Naqa, I., Grigsby, P. W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., ... & Thorstad, W. L. (2009). Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern recognition, 42(6), 1162-1171.
2. 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.
3. Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P. M., Sundarasekar, R., & Hsu, C. H. (2017). Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless personal communications, 1-18.
4. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248.
5. Asiedu, M. N., Simhal, A., Lam, C. T., Mueller, J., Chaudhary, U., Schmitt, J. W., ... & Ramanujam, N. (2018, February). Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope. In Optics and Biophotonics in Low-Resource Settings IV (Vol. 10485, p. 1048508). International Society for Optics and Photonics.
6. Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q. (2018). Deep Learning for Image-based Cancer Detection and Diagnosis—A Survey. Pattern Recognition.
Hope these papers might help you.
Thanks,
Sobhan
Dear Eduard,
You may refer to the following papers:
1. El Naqa, I., Grigsby, P. W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., ... & Thorstad, W. L. (2009). Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern recognition, 42(6), 1162-1171.
2. 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.
3. Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P. M., Sundarasekar, R., & Hsu, C. H. (2017). Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless personal communications, 1-18.
4. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248.
5. Asiedu, M. N., Simhal, A., Lam, C. T., Mueller, J., Chaudhary, U., Schmitt, J. W., ... & Ramanujam, N. (2018, February). Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope. In Optics and Biophotonics in Low-Resource Settings IV (Vol. 10485, p. 1048508). International Society for Optics and Photonics.
6. Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q. (2018). Deep Learning for Image-based Cancer Detection and Diagnosis—A Survey. Pattern Recognition.
Hope these papers might help you.
Thanks,
Sobhan
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