Application of a Deep Learning Model for Effective Diagnosis of Osteosarcoma
DOI:
https://doi.org/10.47611/jsrhs.v13i3.7639Keywords:
Osteosarcoma, Bone cancer, Medical Imaging, Machine Learning, Neural networks, Cancer imagingAbstract
This experiment aimed to test the effects of different osteosarcoma tumor types on the accuracy of a trained neural network. Osteosarcoma is a subset of bone cancer seen greatly in pediatric cases and young adults, and its onset involves the rapid production of abnormal bone tissue, which leads to acute injuries. It is malignant by nature and life-threatening; therefore, it must be detected early by methods including X-ray, CT, MRI, and microscopy. The diversity of neoplastic formation makes classifying difficult, so using neural networks for detection can be beneficial. These machine-learning algorithms can identify patterns and analyze medical images for an accurate diagnosis. A dataset with three osteosarcoma classes was used: Viable Tumor, Non-Viable (Necrotic) Tumor, and Non-Tumor. All images were hematoxylin and eosin (H&E) stained cells used in training and testing. The structure of the network was a pre-trained model: VGG19, which uses the structure of a Convolutional Neural Network (CNN). The structure included the input, 18 convolution layers, max-pooling layers, and hidden layers for feature extraction. The multi-class classification accuracy was 96.27%, proving to be efficient. Large amounts of patient data can be quickly deciphered, and this could lead to advancements in medical imaging and disease prognosis.
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Ahammad, M., Abedin, M. J., Khan, Md. A. R., Alim, Md. A., Rony, M. A. T., Alam, K. M. R., Reza, D. S. A. A., & Uddin, I. (2022). A Proficient Approach to Detect Osteosarcoma Through Deep Learning. 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22). https://doi.org/10.1109/icetet-sip-2254415.2022.9791502
Anisuzzaman, D. M., Barzekar, H., Tong, L., Luo, J., & Yu, Z. (2021). A deep learning study on osteosarcoma detection from histological images. Biomedical Signal Processing and Control, 69, 102931. https://doi.org/10.1016/j.bspc.2021.102931
Arooj, S., Atta-ur-Rahman, Zubair, M., Khan, M. F., Alissa, K., Khan, M. A., & Mosavi, A. (2022). Breast Cancer Detection and Classification Empowered With Transfer Learning. Frontiers in Public Health, p. 10. https://doi.org/10.3389/fpubh.2022.924432
Arunachalam, H. B., Mishra, R., Daescu, O., Cederberg, K., Rakheja, D., Sengupta, A., Leonard, D., Hallac, R., & 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), e0210706. https://doi.org/10.1371/journal.pone.0210706
Bansal, P., Gehlot, K., Singhal, A., & Gupta, A. (2022). Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm. Multimedia Tools and Applications, 81(6), 8807–8834. https://doi.org/10.1007/s11042-022-11949-6
Lindsey, B. A., Markel, J. E., & Kleinerman, E. S. (2016). Osteosarcoma Overview. Rheumatology and Therapy, 4(1), 25–43. https://doi.org/10.1007/s40744-016-0050-2
Mishra, R., Daescu, O., Leavey, P., Rakheja, D., & Sengupta, A. (2017). Histopathological Diagnosis for Viable and Non-viable Tumor Prediction for Osteosarcoma Using Convolutional Neural Network. Bioinformatics Research and Applications, pp. 12–23. https://doi.org/10.1007/978-3-319-59575-7_2
Mishra, R., Daescu, O., Leavey, P., Rakheja, D., & Sengupta, A. (2018). Convolutional Neural Network for Histopathological Analysis of Osteosarcoma. Journal of Computational Biology, 25(3), 313–325. https://doi.org/10.1089/cmb.2017.0153
Mohamed, E. A., Rashed, E. A., Gaber, T., & Karam, O. (2022). Deep learning model for fully automated breast cancer detection system from thermograms. PLOS ONE, 17(1), e0262349. https://doi.org/10.1371/journal.pone.0262349
Nasir, M. U., Khan, S., Mehmood, S., Khan, M. A., Rahman, A., & 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), 5444. https://doi.org/10.3390/s22145444
Pan, L., Wang, H., Wang, L., Ji, B., Liu, M., Chongcheawchamnan, M., Yuan, J., & Peng, S. (2022). Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma. Biomedical Signal Processing and Control, p. 77, 103824. https://doi.org/10.1016/j.bspc.2022.103824
Prater, S., & McKeon, B. (2023, May 23). Osteosarcoma. Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK549868/
Sarker, I. H. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6). https://doi.org/10.1007/s42979-021-00815-1
Yang, S., Zhu, F., Ling, X., Liu, Q., & Zhao, P. (2021). Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Frontiers in Genetics, p. 12. https://doi.org/10.3389/fgene.2021.607471
Zhao, X., Wu, Q., Gong, X., Liu, J., & Ma, Y. (2021). Osteosarcoma: a review of current and future therapeutic approaches. BioMedical Engineering OnLine, 20(1). https://doi.org/10.1186/s12938-021-00860-0
Zhou, X., Wang, H., Feng, C., Xu, R., He, Y., Li, L., & Tu, C. (2022). Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges. Frontiers in Oncology, p. 12. https://doi.org/10.3389/fonc.2022.908873
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