关键词
How to Cite
摘要
Osteosarcoma has become increasingly prevalent globally in recent years, significantly impacting the mortality rates of those affected. Histopathological imaging plays a crucial role in the diagnostic process. The proposed research evaluates the effectiveness of these techniques in accurately detecting necrotic, non-viable, and viable tumors within histopathological tissue. The dataset underwent pretreatment using Gaussian filtering to enhance image quality, followed by formularization techniques to improve model generalization and reduce overfitting. Transfer learning models such as EfficientNetB6, DenseNet201, and MobileNetV2 were employed and trained on histopathological images to improve diagnostic accuracy. Pre-trained models derived from the ImageNet architecture were specifically applied for cancer detection. Additionally, a formularization technique was incorporated into the IGDOOD (Iterative gradient descent Of Osteosarcoma Detection) framework to mitigate overfitting and enhance model performance. Iterative gradient descent was the main optimization algorithm used for training the deep learning model, with the formularization techniques implemented to fine-tune the learning rate improve accuracy, and automatically capture tumor regions to extract the nuclear characteristics of tumor cells. These features to develop a histological image classifier for osteosarcoma, using IGDOOD to predict recurrence and survival rates post-treatment. Performance metrics be approximated to assess the efficiency of osteosarcoma detection with reported accuracy on test data being 95.02 % for EfficientNetB6, 99.10 % for DenseNet201, and 99.40 % for MobileNetV2.
参考
Siegel R.L., Giaquinto A.N., Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024; 74(1): 12-49.-DOI: https://doi.org/10.3322/caac.21820. Erratum in: CA Cancer J Clin. 2024; 74(2): 203.-DOI: https://doi.org/10.3322/caac.21830.
Yin C., Chokkakula S., Li J., et al. Unveiling research trends in the prognosis of osteosarcoma: A bibliometric analysis from 2000 to 2022. Heliyon. 2024; 10(6): e27566.-DOI: https://doi.org/10.1016/j.heliyon.2024.e27566.
Global Cancer Observatory — World Health Organization. International Agency for Research on Cancer (IARC). 2025.-URL: https://gco.iarc.fr/en.
Sharma A., Yadav D.P., Garg H., et al. Bone cancer detection using feature extraction machine learning model. Comput Math Methods Med. 2021; 2021: 7433186.-DOI: https://doi.org/10.1155/2021/7433186.
Anand D., Arulselvi G., Balaji G.N. Detection of tumor affected part from histopathological bone images using morphological classification and recurrent convoluted neural networks. Journal of Pharmaceutical Negative Results. 2022; 4992–5008.-DOI: https://doi.org/10.47750/pnr.2022.13.s09.617.
Ahmed I., Sardar H., Aljuaid H., et al. Convolutional neural network for histopathological osteosarcoma image classification. Computers, Materials & Continua. 2021; 69(3): 3365–81.-DOI: https://doi.org/10.32604/cmc.2021.018486.
Belayneh R., Fourman M.S., Bhogal S., et al. Update on osteosarcoma. Curr Oncol Rep. 2021; 23(6).-DOI: https://doi.org/10.1007/s11912-021-01053-7.
Wang Z., Lu H., Wu Y., et al. Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features. CAAI Transactions on Intelligence Technology. 2023; 8(3): 836–48.-DOI: https://doi.org/10.1049/cit2.12175.
LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015; 521: 436–444.-DOI: https://doi.org/10.1038/nature14539.
Chen X., Chen H., Wan J., et al. An enhanced AlexNet-Based model for femoral bone tumor classification and diagnosis using magnetic resonance imaging. J Bone Oncol. 2024; 48: 100626.-DOI: https://doi.org/10.1016/j.jbo.2024.100626.
Chhabra P., Kumar R., Prasad R., et al. Osteosarcoma cancer detection using machine learning techniques. Innovations in Data Analytics. 2024; 13–28.-DOI: https://doi.org/10.1007/978-981-97-4928-7_2.
Chen W., Han Y., Awais Ashraf M., et al. A patch-based deep learning MRI segmentation model for improving efficiency and clinical examination of the spinal tumor. J Bone Oncol. 2024; 49: 100649.-DOI: https://doi.org/10.1016/j.jbo.2024.100649.
Gawade S., Bhansali A., Patil K., Shaikh D. Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection. Healthcare Analytics. 2023; 3: 100153.-DOI: https://doi.org/10.1016/j.health.2023.100153.
Talukder Md.A., Islam Md.M., Uddin Md.A., et al. An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Systems with Applications. 2023; 230: 120534.-DOI: https://doi.org/10.1016/j.eswa.2023.120534.
Kour P., Mansotra V. Implementation of pretrained models to classify osteosarcoma from histopathological images. Proceedings of International Conference on Recent Innovations in Computing. 2024; 589–603.-DOI: https://doi.org/10.1007/978-981-97-2839-8_41.
Bansal P., Gehlot K., Singhal A., et al. Automatic detection of osteosarcoma based on integrated features and feature selection using a binary arithmetic optimization algorithm. Multimed Tools Appl. 2022; 81: 8807–8834.-DOI: https://doi.org/10.1007/s11042-022-11949-6.
Alabdulkreem E., Saeed M.K., Alotaibi S.S., et al. Bone cancer detection and classification using owl search algorithm with deep learning on x-ray images. IEEE Access. 2023; 11: 109095–103.-DOI: https://doi.org/10.1109/access.2023.3319293.
Alsubai S., Dutta A.K., Alghayadh F., et al. Group teaching optimization with deep learning-driven osteosarcoma detection using histopathological images. IEEE Access. 2024; 12: 34089–98.-DOI: https://doi.org/10.1109/access.2024.3371518.
Aydin Şimşek Ş., Aydin A., Say F., et al. Enhanced enchondroma detection from x‐ray images using deep learning: A step towards accurate and cost‐effective diagnosis. Journal of Orthopaedic Research. 2024; 42(12): 2826–34.-DOI: https://doi.org/10.1002/jor.25938.
Liu F., Zhu J., Lv B., et al. Auxiliary segmentation method of osteosarcoma MRI image based on transformer and U-net. Comput Intell Neurosci. 2022; 2022: 9990092.-DOI: https://doi.org/10.1155/2022/9990092.
Mohan B.C. Osteosarcoma classification using multilevel feature fusion and ensembles. 2021 IEEE 18th India Council International Conference (INDICON). 2021; 1–6.-DOI: https://doi.org/10.1109/indicon52576.2021.9691543.
Misaghi A., Goldin A., Awad M., Kulidjian A.A. Osteosarcoma: a comprehensive review. SICOT J. 2018; 4: 12.-DOI: https://doi.org/10.1051/sicotj/2017028.
Fu Y., Xue P., Ji H., et al. Deep model with Siamese network for viable and necrotic tumor regions assessment in osteosarcoma. Med Phys. 2020; 47(10): 4895-4905.-DOI: https://doi.org/10.1002/mp.14397.
Aljuaid H., Alturki N., Alsubaie N., et al. Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning. Computer Methods and Programs in Biomedicine. 2022; 223: 106951.-DOI: https://doi.org/10.1016/j.cmpb.2022.106951.
Singh O., Kashyap K.L., Singh K.K. Lung and colon cancer classification of histopathology images using convolutional neural network. SN Computer Science. 2024; 5(2).-DOI: https://doi.org/10.1007/s42979-023-02546-x.
Aarthy R., Muthupriya V., Kalpana S., Ashok M.V. Bone cancer detection based on histopathology images using optimized temporal spatial multi scale dilated convolutional neural network with MultiNet. 2024 Advances in Science and Engineering Technology International Conferences (ASET). 2024; 01–8.-DOI: https://doi.org/10.1109/aset60340.2024.10708740.
Das S., Saikia J., Das S., Goni N. A comparative study of different noise filtering techniques in digital images. International Journal of Engineering Research and General Science. 2015; 3(5): 180.-ISSN: 2091-2730.-URL: https://pnrsolution.org/Datacenter/Vol3/Issue5/25.pdf.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
© АННМО «Вопросы онкологии», Copyright (c) 2025