论文标题
对皮肤病变分类的深度学习和机器学习模型和框架的比较
Comparison of Deep Learning and Machine Learning Models and Frameworks for Skin Lesion Classification
论文作者
论文摘要
皮肤癌的发病率在全世界一直在稳步上升，这是一个严重的问题。在早期的诊断可能会大大减少疾病造成的伤害，但是，传统活检是一种劳动密集型和侵入性的手术。此外，许多农村社区不容易获得医院，并且不希望因为他们觉得可能是一个小问题而拜访一个。使用机器学习和深度学习进行皮肤癌分类可以提高可及性，并减少传统病变检测过程中涉及的不适程序。这些模型可以包裹在网络或移动应用中，并为更多的人口提供服务。在本文中，在常见皮肤病变的基准HAM10000数据集上测试了两个这样的模型。它们是带有分层k折的随机森林，而Mobilenetv2（在本文的其余部分称为Mobilenet）。使用Tensorflow和Pytorch框架分别训练Mobilenet模型。深度学习和机器学习模型的并排比较，以及在资源受限的移动环境中对不同框架的相同深度学习模型进行了比较。结果表明，这些模型中的每一个在不同的分类任务上都更好。为了提高整体召回，准确性和恶性黑色素瘤的检测，Tensorflow Mobilenet是更好的选择。但是，为了检测非癌性皮肤病变，Pytorch Mobilenet被证明更好。当涉及到中等正确性的计算成本低时，随机森林是更好的算法。
The incidence rate for skin cancer has been steadily increasing throughout the world, leading to it being a serious issue. Diagnosis at an early stage has the potential to drastically reduce the harm caused by the disease, however, the traditional biopsy is a labor-intensive and invasive procedure. In addition, numerous rural communities do not have easy access to hospitals and do not prefer visiting one for what they feel might be a minor issue. Using machine learning and deep learning for skin cancer classification can increase accessibility and reduce the discomforting procedures involved in the traditional lesion detection process. These models can be wrapped in web or mobile apps and serve a greater population. In this paper, two such models are tested on the benchmark HAM10000 dataset of common skin lesions. They are Random Forest with Stratified K-Fold Validation, and MobileNetV2 (throughout the rest of the paper referred to as MobileNet). The MobileNet model was trained separately using both TensorFlow and PyTorch frameworks. A side-by-side comparison of both deep learning and machine learning models and a comparison of the same deep learning model on different frameworks for skin lesion diagnosis in a resource-constrained mobile environment has not been conducted before. The results indicate that each of these models fares better at different classification tasks. For greater overall recall, accuracy, and detection of malignant melanoma, the TensorFlow MobileNet was the better choice. However, for detecting noncancerous skin lesions, the PyTorch MobileNet proved to be better. Random Forest was the better algorithm when it came to having a low computational cost with moderate correctness.
