Gorilla Troops Optimizer with Deep Learning-Based Thyroid Cancer Classification on Histopathological Images

Gorilla Troops Optimizer with Deep Learning-Based Thyroid Cancer Classification on Histopathological Images
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-2
Year of Publication : 2023
Author : M. Gokilavani, M. Sriram, S.P. Vijayaraghavan, T. Jaya
DOI : 10.14445/22315381/IJETT-V71I2P204

How to Cite?

M. Gokilavani, M. Sriram, S.P. Vijayaraghavan, T. Jaya, "Gorilla Troops Optimizer with Deep Learning-Based Thyroid Cancer Classification on Histopathological Images," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 27-38, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P204

Abstract
The thyroid gland serves a vital role in regulating various body functions, namely energy expenditure, metabolism, and organ function, such as the heart and brain. Thyroid cancer refers to a cancer of the thyroid gland and is a commonest endocrine cancer. A pathologist can detect thyroid carcinoma on the basis of the visual inspection of tissue samples prepared on microscopic slides. Machine learning (ML) is increasingly employed in the medical imaging fields and for pathological diagnosis of various diseases. A deep convolutional neural network (DCNN) is a kind of ML, such as a specific artificial neural network resembling the multi-layered human cognitive system. Various studies have examined the application of DCNN to assess pathological images. This paper introduces a novel Gorilla Troops Optimizer with Deep Learning Based Thyroid Cancer Classification on Histopathological Images (GTODL-TCHI) model. The presented GTODL-TCHI model majorly analyses the HIs for the identification and classification of thyroid disease. Initially, the GTODL-TCHI model applies the image denoising procedure using the non-local mean filtering (NLMF) technique. In addition, the pre-processed images are then segmented using a fully convolutional network (FCN). Besides, the GTO algorithm with a densely connected network (DenseNet121) method can be implied to produce feature vectors. Finally, the classification of features takes place using a stacked sparse autoencoder (SSAE) model. The performance validation of the GTODL-TCHI method can be tested using the HI dataset. The results stated the significant performance over the recent state of art DL models.

Keywords
Thyroid disease, Histopathological images, Computer aided diagnosis, Deep learning, Image processing.

References
[1] Vijaya Gajanan Buddhavarapu, and Angel Arul Jothi J, “An Experimental Study on Classification of Thyroid Histopathology Images Using Transfer Learning,” Pattern Recognition Letters, vol. 140, pp. 1-9, 2020. Crossref, https://doi.org/10.1016/j.patrec.2020.09.020
[2] Bing Han et al., “Automatic Classification Method of Thyroid Pathological Images Using Multiple Magnification Factors,” Neurocomputing, vol. 460, pp. 231-242, 2021. Crossref, https://doi.org/10.1016/j.neucom.2021.07.024
[3] Pingjun Chen et al., “Interactive Thyroid Whole Slide Image Diagnostic System Using Deep Representation,” Computer Methods and Programs in Biomedicine, vol. 195, p. 105630. Crossref, https://doi.org/10.1016/j.cmpb.2020.105630
[4] Junho Song, “Ultrasound Image Analysis Using Deep Learning Algorithm for the Diagnosis of Thyroid Nodules,” Medicine, vol. 98, no. 15, p. e15133, 2019. Crossref, https://doi.org/10.1097%2FMD.0000000000015133
[5] Akila Victor et al., "Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 170-178, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P219
[6] Xiangchun Li et al., “Diagnosis of Thyroid Cancer Using Deep Convolutional Neural Network Models Applied to Sonographic Images: A Retrospective, Multicohort, Diagnostic Study,” The Lancet Oncology, vol. 20, no. 2, pp. 193-201, 2019. Crossref, https://doi.org/10.1016/S1470-2045(18)30762-9
[7] Martin Halicek, “Tumor Detection of the Thyroid and Salivary Glands Using Hyperspectral Imaging and Deep Learning,” Biomedical Optics Express, vol. 11, no. 3, pp. 1383-1400, 2020. Crossref, https://doi.org/10.1364/BOE.381257
[8] Ahmed Sharafeldeen, “Texture and Shape Analysis of Diffusion‐Weighted Imaging for Thyroid Nodules Classification Using Machine Learning,” Medical Physics, vol. 49, no. 2, pp. 988-999, 2022.Crossref, https://doi.org/10.1002/mp.15399
[9] Bijaya Kumar Hatuwal, and Himal Chand Thapa, "Lung Cancer Detection Using Convolutional Neural Network on Histopathological Images," International Journal of Computer Trends and Technology, vol. 68, no. 10, pp. 21-24, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I10P104
[10] Nguyen Thanh Duc et al., “An Ensemble Deep Learning for Automatic Prediction of Papillary Thyroid Carcinoma Using Fine Needle Aspiration Cytology,” Expert Systems with Applications, vol. 188, p. 115927, 2022. Crossref, https://doi.org/10.1016/j.eswa.2021.115927
[11] Bing Han et al., “Automatic Classification Method of Thyroid Pathological Images Using Multiple Magnification Factors,” Neurocomputing, vol. 460, pp. 231-242, 2021. Crossref, https://doi.org/10.1016/j.neucom.2021.07.024
[12] Zihan Wu, “Direct Prediction of BRAFV600E Mutation from Histopathological Images in Papillary Thyroid Carcinoma with a Deep Learning Workflow,” 2020 4th International Conference on Computer Science and Artificial Intelligence, pp. 146-151, 2020. Crossref, https://doi.org/10.1145/3445815.3445840
[13] Yonghua Wang, Wei Ke, and Pin Wan “A Method of Ultrasonic Image Recognition for Thyroid Papillary Carcinoma Based on Deep Convolution Neural Network,” Neuroquantology, vol. 16, no. 5, 2018. Crossref, http://dx.doi.org/10.14704/nq.2018.16.5.1306
[14] Tianjiao Liu et al., “Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features,” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 919-923, 2017. Crossref, https://doi.org/10.1109/ICASSP.2017.7952290
[15] Jianning Chi et al., “Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network,” Journal of Digital Imaging, vol. 30, no. 4, pp. 477-486, 2017. Crossref, https://doi.org/10.1007/s10278-017-9997-
[16] Anish Anurag et al., "Local Attention-Based Descriptor Definition Using Vision Transformer for Breast Cancer Identification," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 317-327, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P230
[17] Babak Kashir, “Application of Fully Convolutional Neural Networks for Feature Extraction in Fluid Flow,” Journal of Visualization, vol. 24, no. 4, pp.771-785, 2021. Crossref, https://doi.org/10.1007/s12650-020-00732-0
[18] Tavishee Chauhan, Hemant Palivela, and Sarveshmani Tiwari, “Optimization and Fine-Tuning of Densenet Model for Classification of Covid-19 Cases in Medical Imaging,” International Journal of Information Management Data Insights, vol. 1, no. 2, p.100020, 2021. Crossref, https://doi.org/10.1016/j.jjimei.2021.100020
[19] J. Antogerminsweeta, and Dr. B. Sivagami, "Contemporary Techniques in Digital Image Processing," SSRG International Journal of Computer Science and Engineering , vol. 6, no. 11, pp. 43-46, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I11P109
[20] Zhezhe Han et al., “Combustion Stability Monitoring Through Flame Imaging and Stacked Sparse Autoencoder Based Deep Neural Network,” Applied Energy, vol. 259, p.114159
[21] Angel Arul Jothi J, and Mary Anita Rajam V, “Automatic Classification of Thyroid Histopathology Images Using Multi-Classifier System,” Multimedia Tools and Applications, vol. 76, no. 18, pp.18711-18730, 2017. Crossref, https://doi.org/10.1007/s11042-017-4363- 0
[22] Kirubha. M et al., "Analysis of Thyroid Disease Using K Means and Fuzzy C Means Algorithm," SSRG International Journal of Computer Science and Engineering, vol. 6, no. 10, pp. 1-6, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I10P101
[23] Mei Yu et al., “Adaptive Soft Erasure with Edge Self-Attention for Weakly Supervised Semantic Segmentation: Thyroid Ultrasound Image Case Study,” Computers in Biology and Medicine, vol. 144, p. 105347, 2022. Crossref, https://doi.org/10.1016/j.compbiomed.2022.105347
[24] Peiling Tsou, and Chang-Jiun Wu, “Mapping Driver Mutations to Histopathological Subtypes in Papillary Thyroid Carcinoma: Applying a Deep Convolutional Neural Network,” Journal of Clinical Medicine, vol. 8, no. 10, p.1675, 2019. Crossref, https://doi.org/10.3390%2Fjcm8101675
[25] M. C. Shanker, and M. Vadivel, "Hybrid Transfer Learning of Mammogram Images for Screening of Micro-Calcifications," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 8, pp. 40-47, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I8P105
[26] Haitao Song et al., “Rapid Identification of Papillary Thyroid Carcinoma and Papillary Microcarcinoma Based on Serum Raman Spectroscopy Combined with Machine Learning Models,” Photodiagnosis and Photodynamic Therapy, vol. 37, p.102647, 2022. https://doi.org/10.1016/j.pdpdt.2021.102647
[27] Ahmed Ginidi et al., “Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems,” Sustainability, vol. 13, no. 16, p. 9459, 2021.