Department of Computer Science Engineering, Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai, Kalavakkam, India.
Exp Oncol. 2024 Oct 9;46(2):119-128. doi: 10.15407/exp-oncology.2024.02.119.
Oral squamous cell carcinoma (OSCC), the 13th most common type of cancer, claimed 364,339 lives in 2020. Researchers have established a strong correlation between early detection and better prognosis for this type of cancer. Tissue biopsy, the most common diagnostic method used by doctors, is both expensive and time-consuming. The recent growth in using transfer learning methodologies to aid in medical diagnosis, along with the improved 5-year survival rate from early diagnosis serve as motivation for this study. The aim of the study was to evaluate an innovative approach using transfer learning of pre-trained classification models and convolutional neural networks (CNN) for the binary classification of OSCC from histopathological images.
The dataset used for the experiments consisted of 5192 histopathological images in total. The following pre-trained deep learning models were used for feature extraction: ResNet-50, VGG16, and InceptionV3 along with a tuned CNN for classification.
The proposed methodologies were evaluated against the current state of the art. A high sensitivity and its importance in the medical field were highlighted. All three models were used in experiments with different hyperparameters and tested on a set of 126 histopathological images. The highest-performance developed model achieved an accuracy of 0.90, a sensitivity of 0.97, and an AUC of 0.94. The visualization of the results was done using ROC curves and confusion matrices. The study further interprets the results obtained and concludes with suggestions for future research.
The study successfully demonstrated the potential of using transfer learning-based methodologies in the medical field. The interpretation of the results suggests their practical viability and offers directions for future research aimed at improving diagnostic precision and serving as a reliable tool to physicians in the early diagnosis of cancer.
口腔鳞状细胞癌（OSCC）是第 13 种最常见的癌症，2020 年导致 364339 人死亡。研究人员已经确定了早期检测与这种癌症更好的预后之间存在很强的相关性。组织活检是医生最常用的诊断方法，既昂贵又耗时。最近，使用迁移学习方法来辅助医学诊断的方法不断增加，以及早期诊断带来的 5 年生存率的提高，为这项研究提供了动力。本研究的目的是评估一种使用预训练分类模型和卷积神经网络（CNN）的迁移学习方法，对组织病理学图像进行口腔鳞状细胞癌的二进制分类。
实验使用的数据集共有 5192 张组织病理学图像。使用以下预训练的深度学习模型进行特征提取：ResNet-50、VGG16 和 InceptionV3，以及一个经过调整的 CNN 进行分类。
该方法与当前的最新技术进行了比较。突出了高灵敏度在医学领域的重要性。在实验中使用了三种不同的超参数模型，在一组 126 张组织病理学图像上进行了测试。性能最高的开发模型的准确率为 0.90，灵敏度为 0.97，AUC 为 0.94。使用 ROC 曲线和混淆矩阵对结果进行了可视化。进一步解释了结果，并得出了未来研究的建议。
本研究成功地证明了在医学领域使用基于迁移学习的方法的潜力。结果的解释表明了它们在实际中的可行性，并为未来旨在提高诊断精度的研究提供了方向，作为医生早期诊断癌症的可靠工具。
