The field of clinical image analysis has been applying transfer learning models increasingly due to their less computational complexity, better accuracy etc. These are pre-trained models that don't require to be trained from scratch which eliminates the necessity of large datasets. Transfer learning models are mostly used for the analysis of brain, breast, or lung images but other sectors such as bone marrow cell detection or bone cancer detection can also benefit from using transfer learning models, especially considering the lack of available large datasets for these tasks. This paper studies the performance of several transfer learning models for osteosarcoma tumour detection. Osteosarcoma is a type of bone cancer mostly found in the cells of the long bones of the body. The dataset consists of H&E stained images divided into 4 categories- Viable Tumor, Non-viable Tumor, Non-Tumor and Viable Non-viable. Both datasets were randomly divided into train and test sets following an 80-20 ratio. 80% was used for training and 20\% for test. 4 models are considered for comparison- EfficientNetB7, InceptionResNetV2, NasNetLarge and ResNet50. All these models are pre-trained on ImageNet. According to the result, InceptionResNetV2 achieved the highest accuracy (93.29%), followed by NasNetLarge (90.91%), ResNet50 (89.83%) and EfficientNetB7 (62.77%). It also had the highest precision (0.8658) and recall (0.8658) values among the 4 models.

翻译：临床图像分析领域越来越多地应用迁移学习模型，因其计算复杂度低、精度高等优势。这些预训练模型无需从零开始训练，从而消除了对大型数据集的需求。迁移学习模型多用于脑部、乳腺或肺部图像分析，但诸如骨髓细胞检测或骨癌检测等其他领域同样受益于迁移学习模型，尤其是在这些任务缺乏可用大型数据集的情况下。本文研究了多种迁移学习模型在骨肉瘤肿瘤检测中的性能表现。骨肉瘤是一种多见于人体长骨细胞中的骨癌类型。数据集由苏木精-伊红染色图像构成，分为四类：活性肿瘤、非活性肿瘤、非肿瘤及活性-非活性。两个数据集均按80-20比例随机划分为训练集和测试集，其中80%用于训练，20%用于测试。研究比较了四种模型：EfficientNetB7、InceptionResNetV2、NasNetLarge和ResNet50。这些模型均在ImageNet上预训练。结果显示，InceptionResNetV2取得了最高准确率（93.29%），其次为NasNetLarge（90.91%）、ResNet50（89.83%）和EfficientNetB7（62.77%）。该模型在四种模型中同样取得了最高的精确率（0.8658）和召回率（0.8658）。