Department of Computer Engineering & Applications, GLA University, NH#2, Delhi Mathura Highway, Post Ajhai, Mathura, (UP), India.
Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India.
Comput Math Methods Med. 2021 Dec 20;2021:7433186. doi: 10.1155/2021/7433186. eCollection 2021.
Bone cancer is considered a serious health problem, and, in many cases, it causes patient death. The X-ray, MRI, or CT-scan image is used by doctors to identify bone cancer. The manual process is time-consuming and required expertise in that field. Therefore, it is necessary to develop an automated system to classify and identify the cancerous bone and the healthy bone. The texture of a cancer bone is different compared to a healthy bone in the affected region. But in the dataset, several images of cancer and healthy bone are having similar morphological characteristics. This makes it difficult to categorize them. To tackle this problem, we first find the best suitable edge detection algorithm after that two feature sets one with hog and another without hog are prepared. To test the efficiency of these feature sets, two machine learning models, support vector machine (SVM) and the Random forest, are utilized. The features set with hog perform considerably better on these models. Also, the SVM model trained with hog feature set provides an 1-score of 0.92 better than Random forest 1-score 0.77.
骨癌被认为是一个严重的健康问题，在许多情况下，它会导致患者死亡。医生使用 X 射线、MRI 或 CT 扫描图像来识别骨癌。这个手动的过程耗时且需要该领域的专业知识。因此，有必要开发一种自动系统来对癌性骨和健康骨进行分类和识别。与受影响区域的健康骨相比，癌性骨的纹理有所不同。但是在数据集里，一些癌症和健康骨的图像具有相似的形态特征。这使得对它们进行分类变得很困难。为了解决这个问题，我们首先找到最合适的边缘检测算法，然后准备了两个特征集，一个带有 hog，另一个没有 hog。为了测试这些特征集的效率，我们使用了两种机器学习模型，支持向量机 (SVM) 和随机森林。带有 hog 的特征集在这些模型上表现得更好。此外，使用 hog 特征集训练的 SVM 模型的 1 分数为 0.92，优于随机森林的 0.77。
