HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS
Kevin Joy DSOUZA
joydsouza33@gmail.comResearch Scholar (Dept. of CSE, PACE Mangalore) (India)
Zahid Ahmed ANSARI
Professor (Dept. of CSE, PACE Mangalore), (India)
Abstract
The healthcare industry is one of the many out there that could majorly benefit from advancement in the technology it utilizes. Artificial intelligence (AI) technologies are especially integral and specifically deep learning (DL); a highly useful data-driven technology. It is applied in a variety of different methods but it mainly depends on the structure of the available data. However, with varying applications, this technology produces data in different contexts with particular connotations. Reports which are the images of scans play a great role in identifying the existence of the disease in a patient. Further, the automation in processing these images using technology like CNN-based models makes it highly efficient in reducing human errors otherwise resulting in large data. Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient. Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) architectures. This study uses the transfer learning technique during training and early stopping criteria are used to avoid overfitting during the training phase. these models use LSTM parallel layer imposed in the model to experiment with four considered architectures such as MobileNet, VGG16, and ResNet with 101 and 152 layers. The experimental results produced by these hybrid models show that the capability of Hybrid ResNet101 and Hybrid ResNet152 architectures are highly suitable with an accuracy of 90% and 92%. Finally, this study concludes that the proposed Hybrid ResNet-152 architecture is highly efficient in classifying the histopathology images. The proposed study has conducted a well-focused and detailed experimental study which will further help researchers to understand the deep CNN architectures to be applied in application development.
Keywords:
breast cancer CNN, loss, accuracy, precision, confusion matrixReferences
Aziz, H. A. (2017). A review of the role of public health informatics in healthcare. Journal of Taibah University Medical Sciences, 12(1), 78–81. https://doi.org/10.1016/J.JTUMED.2016.08.011
DOI: https://doi.org/10.1016/j.jtumed.2016.08.011
Google Scholar
Boumaraf, S., Liu, X., Zheng, Z., Ma, X., & Ferkous, C. (2021). A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomedical Signal Processing and Control, 63, 102192. https://doi.org/10.1016/j.bspc.2020.102192
DOI: https://doi.org/10.1016/j.bspc.2020.102192
Google Scholar
Buddhavarapu, V. G., & Jothi, A. A. J. (2020). An experimental study on classification of thyroid histopathology images using transfer learning. Pattern Recognition Letters, 140, 1–9. https://doi.org/10.1016/j.patrec.2020.09.020
DOI: https://doi.org/10.1016/j.patrec.2020.09.020
Google Scholar
Deep Learning Frameworks. NVIDIA Developer. (n.d.). Retrieved April 3, 2021 from https://developer.nvidia.com/deep-learning-frameworks
Google Scholar
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Li, F.-F. (2010). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). IEEE. https://doi.org/10.1109/CVPR.2009.5206848
DOI: https://doi.org/10.1109/CVPR.2009.5206848
Google Scholar
Djellali, C., Adda, M., & Moutacalli, M. T. (2020). A Data-Driven Deep Learning Model to Pattern Recognition for Medical Diagnosis, by using Model Aggregation and Model Selection. Procedia Computer Science, 177, 387–395. https://doi.org/10.1016/J.PROCS.2020.10.052
DOI: https://doi.org/10.1016/j.procs.2020.10.052
Google Scholar
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Meunier-FitzHugh, K. L., Meunier-FitzHugh, L. C. L., Misra, S., Mogaji, E., Sharma, S. K., Singh, J. B., Raghavan, V., Raman, R., Rana, N. P., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/J.IJINFOMGT.2019.08.002
DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Google Scholar
Eelbode, T., Sinonquel, P., Maes, F., & Bisschops, R. (2021). Pitfalls in training and validation of deep learning systems. Best Practice & Research Clinical Gastroenterology, 52–53, 101712. https://doi.org/10.1016/J.BPG.2020.101712
DOI: https://doi.org/10.1016/j.bpg.2020.101712
Google Scholar
Guan, Q., Wang, Y., Ping, B., Li, D., Du, J., Qin, Y., Lu, H., Wan, X., & Xiang, J. (2019). Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. Journal of Cancer, 10(20), 4876. https://doi.org/10.7150/JCA.28769
DOI: https://doi.org/10.7150/jca.28769
Google Scholar
Haghighat, E., & Juanes, R. (2020). ScienceDirect SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks. Computer Methods in Applied Mechanics and Engineering, 373, 113552. https://doi.org/10.1016/j.cma.2020.113552
DOI: https://doi.org/10.1016/j.cma.2020.113552
Google Scholar
Improving the convergence of back-propagation learning with second-order methods — NYU Scholars. (n.d.). Retrieved March 23, 2022 from https://nyuscholars.nyu.edu/en/publications/improving-theconvergence-of-back-propagation-learning-with-secon
Google Scholar
Kaur, K., & Mittal, S. K. (2020). Classification of mammography image with CNN-RNN based semantic features and extra tree classifier approach using LSTM. Materials Today: Proceedings, in press. https://doi.org/10.1016/j.matpr.2020.09.619
DOI: https://doi.org/10.1016/j.matpr.2020.09.619
Google Scholar
Kaur, P., Singh, G., & Kaur, P. (2019). Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics in Medicine Unlocked, 16, 100151. https://doi.org/10.1016/J.IMU.2019.01.001
DOI: https://doi.org/10.1016/j.imu.2019.01.001
Google Scholar
Leen, T. K., Dietterich, T. G., & Tresp, V. (2001). Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference. MIT Press.
Google Scholar
Liang, R. B., Li, P., Li, B. T., Jin, J. T., Rusch, V. W., Jones, D. R., Wu, Y. L., Liu, Q., Yang, J., Yang, M. Z., Li, S., Long, H., Fu, J. H., Zhang, L. J., Lin, P., Rong, T. H., Hou, X., Lin, S. X., & Yang, H. X. (2021). Modification of Pathologic T Classification for Non-small Cell Lung Cancer With Visceral Pleural Invasion: Data From 1,055 Cases of Cancers ≤ 3 cm. Chest, 160(2), 754–764. https://doi.org/10.1016/J.CHEST.2021.03.022
DOI: https://doi.org/10.1016/j.chest.2021.03.022
Google Scholar
Moon, J. C. C., Perez De Arenaza, D., Elkington, A. G., Taneja, A. K., John, A. S., Wang, D., Janardhanan, R., Senior, R., Lahiri, A., Poole-Wilson, P. A., & Pennell, D. J. (2004). The Pathologic Basis of Q-Wave and Non-Q-Wave Myocardial Infarction: A Cardiovascular Magnetic Resonance Study. Journal of the American College of Cardiology, 44(3), 554–560. https://doi.org/10.1016/J.JACC.2004.03.076
DOI: https://doi.org/10.1016/j.jacc.2004.03.076
Google Scholar
Pramanik, P. K. D., Pal, S., Mukhopadhyay, M., & Singh, S. P. (2021). Big Data classification: techniques and tools. Applications of Big Data in Healthcare, 2021, 1–43. https://doi.org/10.1016/B978-0-12-820203-6.00002-3
DOI: https://doi.org/10.1016/B978-0-12-820203-6.00002-3
Google Scholar
Sarwinda, D, Paradisa, R., Bustamama, A., & Anggiab, P. (2021). Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Computer Science, 179, 423-431. https://doi.org/10.1016/j.procs.2021.01.025
DOI: https://doi.org/10.1016/j.procs.2021.01.025
Google Scholar
Sertolli, B., Ren, Z., Schuller, B. W., & Cummins, N. (2021). Representation transfer learning from deep endto-end speech recognition networks for the classification of health states from speech. Computer Speech and Language, 68, 101204. https://doi.org/10.1016/j.csl.2021.101204
DOI: https://doi.org/10.1016/j.csl.2021.101204
Google Scholar
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. http://www.robots.ox.ac.uk
Google Scholar
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462. https://doi.org/10.1109/TBME.2015.2496264
DOI: https://doi.org/10.1109/TBME.2015.2496264
Google Scholar
TensorFlow Framework & GPU Acceleration. NVIDIA Data Center. (n.d.). Retrieved March 23, 2022 from https://www.nvidia.com/en-sg/data-center/gpu-accelerated-applications/tensorflow/
Google Scholar
Tripathi, S., Singh, S. K., & Lee, H. K. (2021). An end-to-end breast tumour classification model using context-based patch modelling – A BiLSTM approach for image classification. Computerized Medical Imaging and Graphics, 87, 101838. https://doi.org/10.1016/j.compmedimag.2020.101838
DOI: https://doi.org/10.1016/j.compmedimag.2020.101838
Google Scholar
UCI Machine Learning Repository. (n.d.). Retrieved March 23, 2022 from https://archive.ics.uci.edu/ml/index.php
Google Scholar
Xiang, Q., Zhang, G., Wang, X., Lai, J., Li, R., & Hu, Q. (2019). Fruit image classification based on Mobilenetv2 with transfer learning technique. ACM International Conference Proceeding Series (pp. 1–7). Association for Computing Machinery. https://doi.org/10.1145/3331453.3361658
DOI: https://doi.org/10.1145/3331453.3361658
Google Scholar
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE, 109(1), 43–76. https://doi.org/10.1109/JPROC.2020.3004555
DOI: https://doi.org/10.1109/JPROC.2020.3004555
Google Scholar
Authors
Kevin Joy DSOUZAjoydsouza33@gmail.com
Research Scholar (Dept. of CSE, PACE Mangalore) India
Authors
Zahid Ahmed ANSARIProfessor (Dept. of CSE, PACE Mangalore), India
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