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br Charfi S El Ansari
Charfi, S., & El Ansari, M. (2018). Computer-aided diagnosis system for colon abnor-malities detection in wireless ABT-888 endoscopy images. Multimedia Tools and Applications, 77(3), 4047–4064.
Charisis, V. S., & Hadjileontiadis, L. J. (2016). Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images. World Journal of Gastroenterology, 22(39), 8641.
Demanet, L. (2008). The curvelet organization. http://www.curvelet.org/software.
Eid, A., Charisis, V. S., Hadjileontiadis, L. J., & Sergiadis, G. D. (2013). A curvelet-based lacunarity approach for ulcer detection from wireless capsule endoscopy images. In Computer-based medical systems (CBMS), 2013 IEEE 26th international symposium on (pp. 273–278). IEEE.
Fawcett, T. (2004). Roc graphs: Notes and practical considerations for researchers.
Gould, H., Tobochnik, J., & Wolfgang, C. (2005). An introduction to computer simula-tion methods: Applications to physical systems ((3rd edition)). Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
Hoshen, J., & Kopelman, R. (1976). Percolation and cluster distribution. i. cluster multiple labeling technique and critical concentration algorithm. Physical Review B, 14(8), 3438.
IARC (2012). Cancer fact sheets: Colorectal cancer. Technical Report. Lyon, France:
International Agency for Research on Cancer.
INCA (2017). Estimate/2018 cancer incidence in Brazil. Technical Report. Rio de Janeiro, Brazil: Instituto Nacional de Câncer José Alencar Gomes da Silva.
John, G. H., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the eleventh conference on uncertainty in artificial in-telligence (pp. 338–345). Morgan Kaufmann Publishers Inc.
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques.
Mandelbrot, B. B. (1983). The fractal geometry of nature: 173. WH freeman New York. Masood, K., & Rajpoot, N. (2009). Texture based classification of hyperspectral colon biopsy samples using clbp. In Biomedical imaging: From nano to macro, 2009.
Psychology.
Mohammed, H. R., & Katran, L. F. (2018). Hybrid method for detection of brain tu-mor using fuzzy c-mean clustering and discrete curve let transform. Interna-tional Journal of Applied Engineering Research, 13(3), 1670–1674.
Naiyar, M., Asim, Y., & Shahid, A. (2015). Automated colon cancer detection using structural and morphological features. In Frontiers of information technology (fit), 2015 13th international conference on (pp. 240–245). IEEE.
Nayak, D. R., Dash, R., Majhi, B., & Prasad, V. (2017). Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach. Expert Systems with Applications, 88, 152–164.
Rabidas, R., Midya, A., Chakraborty, J., Sadhu, A., & Arif, W. (2018). Multi-resolu-tion analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification. In Medical imaging 2018: Computer-aided diagnosis: 10575 (p. 105752N). International Society for Optics and Photonics.
& Faria, P. R. (2017). Features based on the percolation theory for quantification of non-hodgkin lymphomas. Computers in Biology and Medicine, 91, 135–147.
Saraswathi, D., Dharani, D., & Srinivasan, E. (2016). An e cient feature extraction technique for breast cancer diagnosis using curvelet transform and swarm intel-ligence. In Wireless communications, signal processing and networking (WiSPNET), international conference on (pp. 441–445). IEEE.
Saraswathi, D., & Srinivasan, E. (2017). A high-sensitivity computer-aided system for detecting microcalcifications in digital mammograms using curvelet fractal texture features. Computer Methods in Biomechanics and Biomedical Engineering, 5(4), 263–273.