International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue IV Apr 2023- Available at www.ijraset.com

[95] Li, J., Jia, J., Xu, D.: Unsupervised representation learning of image-based plant disease with deep convolutional generative adversarial networks. 2018 37th Chinese Control Conference (CCC), IEEE (2018). DOI: 10.23919/ChiCC.2018.8482813

[96] Li, Z., Guo, R., Li, M., Chen, Y., Li, G.: A review of computer vision technologies for plant phenotyping. Computers and Electronics in Agriculture, 176, 105672 (2020). https://doi.org/10.1016/j.compag.2020.105672

[97] Talo, M., Baloglu, U.B., Yıldırım, Ö., Acharya, U.R.: Application of deep transfer learning for automated brain abnormality classification using MR images. Cognitive Systems Research, 54, 176-188 (2019). https://doi.org/10.1016/j.cogsys.2018.12.007

[98] Mo, J., Zhang, L.: Multi-level deep supervised networks for retinal vessel segmentation. International journal of computer assisted radiology and surgery, 12(12), 2181-2193 (2017). https://doi.org/10.1007/s11548-017-1619-0

[99] Fraz, M.M., Remagnino, P., Hoppe, A., Velastin, S., Uyyanonvara, B., Barman, S.A.: A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features. 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE (2011). DOI: 10.1109/ICSIPA.2011.6144129

[100] Dhanachandra, N., Manglem, K., Chanu, Y.J.: Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771 (2015). https://doi.org/10.1016/j.procs.2015.06.090

[101] Alhussein, M., Aurangzeb, K., Haider, S.I.: An unsupervised retinal vessel segmentation using Hessian and intensity based approach. IEEE Access, 8, 165056-165070 (2020). DOI: 10.1109/ACCESS.2020.3022943

[102] Sulaiman, S.N., Isa, N.A.M.: Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Transactions on Consumer Electronics, 56(4), 2661-2668 (2010). DOI: 10.1109/TCE.2010.5681154

[103] Uhlmann, V., Singh, S., Carpenter, A.E.: CP-CHARM: segmentation-free image classification made accessible. BMC bioinformatics, 17(1), 1-12 (2016). https://doi.org/10.1186/s12859-016-0895-y

[104] Lupaşcu, C.A., Tegolo, D.: Automatic unsupervised segmentation of retinal vessels using self-organizing maps and k-means clustering. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-21946-7_21

[105] Ghosal, A., Nandy, A., Das, A.K., Goswami, S., Panday, M.: A short review on different clustering techniques and their applications. Emerging technology in modelling and graphics, 69-83 (2020). https://doi.org/10.1007/978-981-13-7403-6_9

[106] Ibtehaz, N., Rahman, M.S.: MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121, 74-87 (2020). https://doi.org/10.1016/j.neunet.2019.08.025

[107] Li, S., Yang, B., Hu, J.: Performance comparison of different multi-resolution transforms for image fusion. Information Fusion, 12(2), 7484 (2011). https://doi.org/10.1016/j.inffus.2010.03.002

[108] He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Computer methods and programs in biomedicine, 107(3), 538-556 (2012). https://doi.org/10.1016/j.cmpb.2011.12.007 https://doi.org/10.1007/978-1-4419-6676-6_6

[109] Borgonovo, E.: Sensitivity analysis. An Introduction for the Management Scientist. International Series in Operations Research and Management Science, Cham, Switzerland: Springer (2017).

[110] Komander, D.: Mechanism, specificity and structure of the deubiquitinases. Conjugation and Deconjugation of Ubiquitin Family Modifiers, pp. 69-87. Springer, New York, NY (2010).

[111] Stull, K.E., Tise, M.L., Ali, Z., Fowler, D.R.: Accuracy and reliability of measurements obtained from computed tomography 3D volume rendered images. Forensic science international, 238, 133-140 (2014). https://doi.org/10.1016/j.forsciint.2014.03.005

[112] Jin, Q., Chen, Q., Meng, Z., Wang, B., Su, R.: Construction of retinal vessel segmentation models based on convolutional neural network. Neural Processing Letters, 52(2), 1005-1022 (2020). https://doi.org/10.1007/s11063-019-10011-1

[113] Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics, 34(3), 213-227 (2010). https://doi.org/10.1016/j.compmedimag.2009.09.006

[114] Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE transactions on image processing, 10(7), 1010-1019 (2001). DOI: 10.1109/83.931095