• Journal of Innovative Optical Health Sciences
  • Vol. 14, Issue 6, 2150018 (2021)
Sang Hee Jo1, Yoonhee Kim2, Yoon Bum Lee3, Sung Suk Oh2、*, and Jong-ryul Choi2
Author Affiliations
  • 1School of Biomedical Engineering Daegu Catholic University (DCU) Gyeongsan, 38430, Republic of Korea
  • 2Medical Device Development Center Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF) Daegu 41061, Republic of Korea
  • 3Laboratory Animal Center Daegu-Gyeongbuk Medical Innovation Foundation (DGMIF), Daegu 41061, Republic of Korea
  • show less
    DOI: 10.1142/s1793545821500188 Cite this Article
    Sang Hee Jo, Yoonhee Kim, Yoon Bum Lee, Sung Suk Oh, Jong-ryul Choi. A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images[J]. Journal of Innovative Optical Health Sciences, 2021, 14(6): 2150018 Copy Citation Text show less
    References

    [1] T. T. Mitchell, Machine Learning (McGraw Hill, United States of America, 1997).

    [2] J. Patel, S. Shah, P. Thakkar, K. Kotecha, "Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques," Expert Syst. Appl. 42, 259–268 (2015).

    [3] A. E. Maxwell, T. A. Warner, F. Fang, "Implementation of machine-learning classification in remote sensing: an applied review," Int. J. Remote Sens. 39, 2784–2817 (2018).

    [4] P. Ma, J. Ma, X. Wang, L. Yang, N. Wang, "Deformable convolutional networks for multi-view 3D shape classification," Electron. Lett. 54, 1373– 1375 (2018).

    [5] A. Caggiano, J. Zhang, V. Alfieri, F. Caiazzo, R. Gao, R. Teti, "Machine learning-based image processing for on-line defect recognition in additive manufacturing," CIRP Ann. 68, 451–454 (2019).

    [6] H. Ma, T. Celik, "FER-Net: Facial expression recognition using densely connected convolutional network," Electron. Lett. 55, 184–186 (2019).

    [7] T. B. Alakus, I. Turkoglu, "Emotion recognition with deep learning using GAMEEMO data set," Electron. Lett. 56, 1364–1367 (2020).

    [8] H. Zhang, C.-L. Hung, M. Liu, X. Hu, Y.-Y. Lin, "NCNet: Deep learning network models for predicting function of non-coding DNA," Front. Genet. 10, 432 (2019).

    [9] G. Mata, M. Radojevi?, C. Fernandez-Lozano, I. Smal, N. Werij, M. Morales, E. Meijering, J. Rubio, "Automated neuron detection in high-content fluorescence microscopy images using machine learning," Neuroinformatics 17, 253–269 (2019).

    [10] S. ? Yeti?, A. ?apar, D. A. Ekinci, U. E. Ayten, B. E. Kerman, B. U. T€oreyinb, "Myelin detection in fluorescence microscopy images using machine learning," J. Neurosci. Methods 346, 108946 (2020).

    [11] Y. Rivenson, Z. G€or€ocs, H. Günaydin, Y. Zhang, H. Wang, A. Ozcan, "Deep learning microscopy," Optica 4, 1437–1443 (2017).

    [12] H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günayd?n, L. A. Bentolila, C. Kural, A. Ozcan, "Deep learning enables cross-modality super-resolution in fluorescence microscopy," Nat. Methods 16, 103–110 (2019).

    [13] L. Fang, F. Monroe, S. W. Novak, L. Kirk, C. R. Schiavon, S. B. Yu, T. Zhang, M. Wu, K. Kastner, A. A. Latif, Z. Lin, A. Shaw, Y. Kubota, J. Mendenhall, Z. Zhang, G. Pekkurnaz, J. Mendenhall, K. Harris, J. Howard, U. Manor, "Deep learning-based point-scanning super-resolution imaging," Nat. Methods 18, 406–416 (2021).

    [14] P. Lakhani, A. B. Prater, R. K. Hutson, K. P. Andriole, K. J. Dreyer, J. Morey, L. M. Prevedello, T. J. Clark, J. R. Geis, J. N. Itri, C. M. Hawkins, "Machine learning in radiology: applications beyond image interpretation," J. Am. Coll. Radiol. 15, 350– 359 (2018).

    [15] T. T. Tang, J. A. Zawaski, K. N. Francis, A. A. Qutub, M. W. Gaber, "Image-based classification of tumor type and growth rate using machine learning: a preclinical study," Sci. Rep. 9, 12529 (2019).

    [16] A. Janowczyk, A. Madabhushi, "Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases," J. Pathol. Inform. 7, 29 (2016).

    [17] T. Schlegl, P. Seeb€ock, S. M. Waldstein, U. Schmidt-Erfurth, G. Langs, Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, Int. Conf. on Information Processing in Medical Imaging, pp. 146–157, Springer, Chem (2017).

    [18] A. Serag, A. Ion-Margineanu, H. Qureshi, R. McMillan, M. S. Martin, J. Diamond, P. O'Reilly, P. Hamilton, "Translational AI and deep learning in diagnostic pathology," Front. Med. 6, 185 (2019).

    [19] Y. Zeng, J. Zhang, "A machine learning model for detecting invasive ductal carcinoma with Google Cloud AutoML Vision," Compute. Biol. Med. 122, 103861 (2020).

    [20] T. Xia, A. Kumar, D. Feng, J. Kim, Patch-level tumor classification in digital histopathology images with domain adapted deep learning, 40th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 644–647, IEEE (2018).

    [21] Y. Kim, Y. B. Lee, S. K. Bae, S. S. Oh, J. Choi, "Development of a photochemical thrombosis investigation system to obtain a rabbit ischemic stroke model," Sci. Rep. 11, 5787 (2021).

    [22] D. R. Cox, "The regression analysis of binary sequences," J. R. Stat. Soc. Series B 20, 215–232 (1958).

    [23] D. Jurafsky, J. Martin, "Logistic regression," Speech and Language Processing, Chap. 5, 3rd Edition, Draft, https://web.stanford.edu/jurafsky/slp3/5.pdf.

    [24] S. Kim, "Logistic (regression) classifier," https:// docs.google.com/presentation/d/180ZISPNRVWYKyV61xoZepZ KVUK6mujIXuwXE0eKZuM/ edit#slide=id.g1ed121957d 0 0.

    [25] X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proc. 13th Int. Conf. on Artificial Intelligence and Statistics, pp. 249–256, JMLR (2010).

    [26] K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, Proc. IEEE Int. Conf. on Computer Vision, pp. 1026–1034, IEEE, (2015).

    [27] D. P. Kingma, J. Ba, "Adam: A method for stochastic optimization," arXiv: 1412.6980.

    [28] Gradient Descent Optimization Algorithms Overview, http://shuuki4.github.io/deep%20learning/ 2016/05/20/Gradient-Descent-Algorithm-Overview. html.

    [29] F. F.Ting, Y. J. Tan, K. S. Sim, "Convolutional neural network improvement for breast cancer classification," Expert Syst. Appl. 120, 103–115 (2019).

    [30] D. Mojahed, R. S. Ha, P. Chang, Y. Gan, X. Yao, B. Angelini, H. Hibshoosh, B. Taback, C. P. Hendon, "Fully automated postlumpectomy breast margin assessment utilizing convolutional neural network based optical coherence tomography image classifi- cation method," Acad. Radiol. 27, e81–e86 (2020).

    [31] S. K. Khara, A. Nishad, A. Upadhyay, V. Bajaj, "Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network," Electron. Lett. 56, 1359–1361 (2020).

    [32] M. Gour, S. Jain, T. S. Kumar, "Residual learning based CNN for breast cancer histopathological image classification," Int. J. Imag. Syst. Technol. 30, 621–635 (2020).

    [33] J. Lyu, X. Bi, S. H. Ling, "Multi-level cross residual network for lung nodule classification," Sensors 20, 2837 (2020).

    [34] S. Liu, Q. Wang, G. Zhang, J. Du, B. Hu, Z. Zhang, "Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network," Anal. Methods 12, 3844–3853 (2020).

    Sang Hee Jo, Yoonhee Kim, Yoon Bum Lee, Sung Suk Oh, Jong-ryul Choi. A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images[J]. Journal of Innovative Optical Health Sciences, 2021, 14(6): 2150018
    Download Citation