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.

本文探讨了使用多种迁移学习模型进行骨肉瘤检测的性能，结果显示使用 InceptionResNetV2 模型获得了最佳的准确度 (93.29%) 和精度 (0.8658)。

利用迁移学习模型进行骨肉瘤肿瘤检测

Osteosarcoma Tumor Detection using Transfer Learning Models

Objective: This paper proposes a deep learning model for breast cancer
detection from reconstructed images of microwave imaging scan data and aims to
improve the accuracy and efficiency of breast tumor detection, which could have
a significant impact on breast cancer diagnosis and treatment. Methods: Our
framework consists of different convolutional neural network (CNN)
architectures for feature extraction and a region-based CNN for tumor
detection. We use 7 different architectures: DenseNet201, ResNet50,
InceptionV3, InceptionResNetV3, MobileNetV2, NASNetMobile and NASNetLarge and
compare its performance to find the best architecture out of the seven. An
experimental dataset of MRI-derived breast phantoms was used. Results:
NASNetLarge is the best architecture which can be used for the CNN model with
accuracy of 88.41% and loss of 27.82%. Given that the model's AUC is 0.786, it
can be concluded that it is suitable for use in its present form, while it
could be improved upon and trained on other datasets that are comparable.
Impact: One of the main causes of death in women is breast cancer, and early
identification is essential for enhancing the results for patients. Due to its
non-invasiveness and capacity to produce high-resolution images, microwave
imaging is a potential tool for breast cancer screening. The complexity of
tumors makes it difficult to adequately detect them in microwave images. The
results of this research show that deep learning has a lot of potential for
breast cancer detection in microwave images

本文旨在提供一种基于 CNN 和深度学习的乳腺癌检测模型，该模型通过重建微波成像扫描数据并使用多种不同的 CNN 架构进行特征提取和肿瘤检测，最终得出使用 NASNetLarge 模型的 CNN 模型具有较高准确率和较低损失的结论，为微波成像技术在乳腺癌筛查中的应用提供了新的研究方向。