Volume 5 • Issue 1 • PP: 40-55 • 2024
Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning
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Intelligent data processing and mining of histopathological images involve the application of advanced techniques and algorithms to analyze and extract meaningful information from digital pathology images. Osteosarcoma is a general malignant bone cancer generally established in teenagers and children. Manual diagnoses of osteosarcoma is a laborious task and needs skilled professionals. The mortality rate can be minimalized only if it is identified on time. Automatic detection systems and new technologies were utilized to classify and analyze medical images that, minimalize the dependency on specialists and result in fast processing. Recently, a lot of Computer-Aided Diagnosis (CAD) systems were proposed by research workers to diagnose and segment osteosarcoma from medical images. Deep learning (DL) algorithms are employed for the automated recognition and identification of osteosarcoma on histopathological images (HSI). The study proposes an Improved Tunicate Swarm Algorithm with Deep Learning for Osteosarcoma Detection and Classification (ITSA-DLODC) approach on pathological imageries. The proposed ITSA-DLODC method mainly enhances the recognition and classification of osteosarcoma on HSI. To attain this, the presented ITSA-DLODC method performs feature extraction using ShuffleNet convolutional neural network model. Besides, the ITSA-based hyperparameter optimizer is exploited to finetune the hyperparameters of the ShuffleNet model. Moreover, the salp swarm algorithm (SSA) with convolutional autoencoder (CAE) approach was utilized for the recognition and identification of osteosarcoma. A wide range of analyses can be applied to exemplify the higher performance of the ITSA-DLODC methodology. The simulation study demonstrated the development of the ITSA-DLODC methodology over other present models
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References
[1] Arunachalam, H.B., Mishra, R., Daescu, O., Cederberg, K., Rakheja, D., Sengupta, A., Leonard, D., Hallac, R. and Leavey, P., 2019. Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models. PloS one, 14(4), p.e0210706.
[2] Tang, H., Sun, N. and Shen, S., 2021. Improving generalization of deep learning models for diagnostic pathology by increasing variability in training data: Experiments on osteosarcoma subtypes. Journal of Pathology Informatics, 12(1), p.30.
[3] Nasir, M.U., Khan, S., Mehmood, S., Khan, M.A., Rahman, A.U. and Hwang, S.O., 2022. IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing. Sensors, 22(14), p.5444.
[4] Patkar, S., Beck, J., Harmon, S., Mazcko, C., Turkbey, B., Choyke, P., Brown, G.T. and LeBlanc, A., 2023. Deep domain adversarial learning for species-agnostic classification of histologic subtypes of osteosarcoma. The American Journal of Pathology, 193(1), pp.60-72.
[5] Mahore, S., Bhole, K. and Rathod, S., 2021, July. Comparative analysis of machine learning algorithm for classification of different osteosarcoma types. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
[6] Ho, D.J., Agaram, N.P., Schüffler, P.J., Vanderbilt, C.M., Jean, M.H., Hameed, M.R. and Fuchs, T.J., 2020, October. Deep interactive learning: an efficient labeling approach for deep learning-based osteosarcoma treatment response assessment. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 540-549). Springer, Cham.
[7] D’Acunto, M., Martinelli, M. and Moroni, D., 2019. From human mesenchymal stromal cells to osteosarcoma cells classification by deep learning. Journal of Intelligent & Fuzzy Systems, 37(6), pp.7199-7206.
[8] Varalakshmi, P., Priyamvadan, A.V. and Rajakumar, B.R., 2022, January. Predicting Osteosarcoma using eXtreme Gradient Boosting Model. In 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-6). IEEE.
[9] Wu, J., Yang, S., Gou, F., Zhou, Z., Xie, P., Xu, N. and Dai, Z., 2022. Intelligent segmentation medical assistance system for mri images of osteosarcoma in developing countries. Computational and Mathematical Methods in Medicine, 2022.
[10] Mahore, S., Bhole, K. and Rathod, S., 2021, August. Machine Learning approach to classify and predict different Osteosarcoma types. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 641-645). IEEE.
[11] Pan, L., Wang, H., Wang, L., Ji, B., Liu, M., Chongcheawchamnan, M., Yuan, J. and Peng, S., 2022. Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma. Biomedical Signal Processing and Control, 77, p.103824.
[12] Vaiyapuri, T., Jothi, A., Narayanasamy, K., Kamatchi, K., Kadry, S. and Kim, J., 2022. Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model. Cancers, 14(24), p.6066.
[13] Gou, F., Liu, J., Zhu, J. and Wu, J., 2022, November. A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning. In Healthcare (Vol. 10, No. 11, p. 2189). Multidisciplinary Digital Publishing Institute.
[14] Anisuzzaman, D.M., Barzekar, H., Tong, L., Luo, J. and Yu, Z., 2021. A deep learning study on osteosarcoma detection from histological images. Biomedical Signal Processing and Control, 69, p.102931.
[15] Badashah, S.J., Basha, S.S., Ahamed, S.R. and Subba Rao, S.P.V., 2021. Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion. International Journal of Intelligent Systems, 36(10), pp.6007-6031.
[16] Fu, Y., Xue, P., Ji, H., Cui, W. and Dong, E., 2020. Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma. Medical Physics, 47(10), pp.4895-4905.
[17] Charchekhandra, B. (2023). The Reading and Analyzing Of The Brain Electrical Signals To Execute a Control Command and Move an Automatic Arm. Pure Mathematics for Theoretical Computer Science, 1( 1), 08-16.
[18] Ullah, N., Khan, J.A., El-Sappagh, S., El-Rashidy, N. and Khan, M.S., 2023. A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network. Diagnostics, 13(1), p.162.
[19] Reddy, C.S.R., Prasanth, B.V. and Chandra, B.M., 2023. Active power management of grid-connected PV-PEV using a Hybrid GRFO-ITSA technique. Science and Technology for Energy Transition, 78, p.7.
[20] Kowsari, K., Sali, R., Ehsan, L., Adorno, W., Ali, A., Moore, S., Amadi, B., Kelly, P., Syed, S. and Brown, D., 2020. Hmic: Hierarchical medical image classification, a deep learning approach. Information, 11(6), p.318.
[21] Kristiyanti, D.A., Sitanggang, I.S. and Nurdiati, S., 2023. Feature Selection Using New Version of V-Shaped Transfer Function for Salp Swarm Algorithm in Sentiment Analysis. Computation, 11(3), p.56.
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