A Comprehensive Review of Transfer Learning Models for Automated Detection of Soft Tissue Sarcoma and Osteosarcoma
DOI:
https://doi.org/10.29284/qppbqp76Keywords:
Sarcoma Detection, Osteosarcoma Classification, Machine Learning in Medical Imaging, Transfer Learning, Deep Learning for Cancer Diagnosis, Medical Image Analysis.Abstract
This is a review of a wide range of research papers that are dedicated to the use of machine learning (ML) in cancer detection. The review summarizes the techniques used in different studies and demonstrates that the significance of ML plays a crucial role in properly identifying tumors in different medical imaging techniques including MRI, CT scans, and histopathological images. The observations by the paper utilising convolutional neural networks (CNNs), deep learning frameworks and ensemble algorithms all demonstrate the importance of these approaches in accurate image analysis, segmentation and classification of tumours. In addition, this review engages in the compilation of multimodal data and the application of radiomic features to multifactorial characterization of a tumor. It also puts an emphasis on the ML-driven clinical decision support systems, explaining how they can help healthcare professionals make accurate diagnostic tests, individualized treatment plans, and forecast treatment results. The discussion also illuminates the existing constraints, ethical issues, and future outlooks of cancer detection using AI/ML. Identifying weaknesses in the interpretability of ideas and the necessity of strong regulatory measures, the review highlights how the ML will transform the diagnostics of cancer and preconditions further innovation in the sphere of this important area of medical practice.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
