Otwarty dostęp

Snake Optimization with deep learning enabled disease detection model for colorectal cancer


Zacytuj

Liu, L., Toung, J.M., Jassowicz, A.F., Vijayaraghavan, R., Kang, H., Zhang, R., Kruglyak, K.M., Huang, H.J., Hinoue, T., Shen, H. and Salathia, N.S., 2018. Targeted methylation sequencing of plasma cell-free DNA for cancer detection and classification. Annals of Oncology, 29(6), pp.1445-1453. Search in Google Scholar

Valle, L., Vilar, E., Tavtigian, S.V. and Stoffel, E.M., 2019. Genetic predisposition to colorectal cancer: syndromes, genes, classification of genetic variants and implications for precision medicine. The Journal of pathology, 247(5), pp.574-588. Search in Google Scholar

Lichtenstern, C.R., Ngu, R.K., Shalapour, S. and Karin, M., 2020. Immunotherapy, inflammation and colorectal cancer. Cells, 9(3), p.618. Search in Google Scholar

Joanito, I., Wirapati, P., Zhao, N., Nawaz, Z., Yeo, G., Lee, F., Eng, C.L., Macalinao, D.C., Kahraman, M., Srinivasan, H. and Lakshmanan, V., 2022. Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer. Nature genetics, 54(7), pp.963-975. Search in Google Scholar

Kather, J.N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.A., Gaiser, T., Marx, A., Valous, N.A., Ferber, D. and Jansen, L., 2019. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS medicine, 16(1), p.e1002730. Search in Google Scholar

Pacal, I., Karaboga, D., Basturk, A., Akay, B. and Nalbantoglu, U., 2020. A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126, p.104003. Search in Google Scholar

Sirinukunwattana, K., Domingo, E., Richman, S.D., Redmond, K.L., Blake, A., Verrill, C., Leedham, S.J., Chatzipli, A., Hardy, C., Whalley, C.M. and Wu, C.H., 2021. Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut, 70(3), pp.544-554. Search in Google Scholar

Kather, J.N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.A., Gaiser, T., Marx, A., Valous, N.A., Ferber, D. and Jansen, L., 2019. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS medicine, 16(1), p.e1002730. Search in Google Scholar

Mitsala, A., Tsalikidis, C., Pitiakoudis, M., Simopoulos, C. and Tsaroucha, A.K., 2021. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Current Oncology, 28(3), pp.1581-1607. Search in Google Scholar

Ho, C., Zhao, Z., Chen, X.F., Sauer, J., Saraf, S.A., Jialdasani, R., Taghipour, K., Sathe, A., Khor, L.Y., Lim, K.H. and Leow, W.Q., 2022. A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Scientific Reports, 12(1), pp.1-9. Search in Google Scholar

Sarwinda, D., Paradisa, R.H., Bustamam, A. and Anggia, P., 2021. Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science, 179, pp.423-431. Search in Google Scholar

Javed, S., Mahmood, A., Fraz, M.M., Koohbanani, N.A., Benes, K., Tsang, Y.W., Hewitt, K., Epstein, D., Snead, D. and Rajpoot, N., 2020. Cellular community detection for tissue phenotyping in colorectal cancer histology images. Medical image analysis, 63, p.101696. Search in Google Scholar

Masud, M., Sikder, N., Nahid, A.A., Bairagi, A.K. and AlZain, M.A., 2021. A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors, 21(3), p.748. Search in Google Scholar

Lorenzovici, N., Dulf, E.H., Mocan, T. and Mocan, L., 2021. Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach. Diagnostics, 11(3), p.514. Search in Google Scholar

Zhou, C., Jin, Y., Chen, Y., Huang, S., Huang, R., Wang, Y., Zhao, Y., Chen, Y., Guo, L. and Liao, J., 2021. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Computerized Medical Imaging and Graphics, 88, p.101861. Search in Google Scholar

Tsai, M.J. and Tao, Y.H., 2021. Deep learning techniques for the classification of colorectal cancer tissue. Electronics, 10(14), p.1662. Search in Google Scholar

Alqudah, A.M. and Alqudah, A., 2022. Improving machine learning recognition of colorectal cancer using 3D GLCM applied to different color spaces. Multimedia Tools and Applications, 81(8), pp.10839-10860. Search in Google Scholar

Mulenga, M., Kareem, S.A., Sabri, A.Q.M., Seera, M., Govind, S., Samudi, C. and Mohamad, S.B., 2021. Feature extension of gut microbiome data for deep neural network-based colorectal cancer classification. IEEE Access, 9, pp.23565-23578. Search in Google Scholar

Yu, H., He, F. and Pan, Y., 2020. A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation. Multimedia Tools and Applications, 79(9), pp.5743-5765. Search in Google Scholar

Mujahid, M., Rustam, F., Álvarez, R., Luis Vidal Mazón, J., Díez, I.D.L.T. and Ashraf, I., 2022. Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network. Diagnostics, 12(5), p.1280. Search in Google Scholar

Hashim, F.A.; Hussien, A.G. Snake Optimizer: A novel meta-heuristic optimization algorithm. Knowl.-Based Syst. 2022, 242, 108320. Search in Google Scholar

Rawa, M., 2022. Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm. Mathematics, 10(8), p.1323. Search in Google Scholar

Yang, X., Wang, W., Ma, J.L., Qiu, Y.L., Lu, K., Cao, D.S. and Wu, C.K., 2022. BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution. Briefings in Bioinformatics, 23(1), p.bbab491. Search in Google Scholar

https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/download/ Search in Google Scholar

K. Sirinukunwattana, D.R.J. Snead, N.M. Rajpoot, “A Stochastic Polygons Model for Glandular Structures in Colon Histology Images,” in IEEE Transactions on Medical Imaging, 2015 doi: 10.1109/TMI.2015.2433900 Search in Google Scholar

Ragab, M. and Albukhari, A., 2022. Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model. CMC-COMPUTERS MATERIALS & CONTINUA, 72(3), pp.5577-5591. Search in Google Scholar