Architectures for Scalable Image
Architectures for Scalable Image
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2021
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基于卷积神经网络的鼹鼠图像，该网络是为 Android 操作系统开发的。该移动应用程序包含黑色素瘤检测功能、先前检查的历史记录以及按病变位置分组的先前检查图像图库。基于 HAM10000 的训练数据集补充了国际皮肤成像合作档案中的黑色素瘤图像，以消除类别不平衡并提高网络准确性。对现有的提供高精度的神经网络进行了搜索，并选择了VGG16、MobileNet和NASNetMobile神经网络进行研究。迁移学习和微调已应用于给定的神经网络，以使网络适应皮肤病变分类的任务。可以确定的是，使用这些技术可以使神经网络获得用于此任务的高精度。描述了使用 TensorFlow Lite 将卷积神经网络转换为优化的 Flatbuffer 格式以在移动设备上放置和使用的过程。根据CPU和GPU上的分类时间来评估所选神经网络在移动设备上的性能特征，并比较单个网络的文件占用的内存量。比较转换前后的神经网络文件大小。事实证明，使用 TensorFlow Lite 转换器可以通过使用优化的格式显着减小神经网络的文件大小，而不会影响其准确性。研究结果表明设备上的应用速度快且网络紧凑，并且使用图形加速可以显着减少肿瘤的图像分类时间。根据分析的参数，选择 NASNetMobile 作为用于黑色素瘤检测移动应用的最佳神经网络。
image of a mole based on a convolutional neural network, which is developed for the Android operating system. The mobile application contains melanoma detection functions, history of the previous examinations and a gallery with images of the previous examinations grouped by the location of the lesion. The HAM10000-based training dataset has been supplemented with the images of melanoma from the archive of The International Skin Imaging Collaboration to eliminate class imbalances and improve network accuracy. The search for existing neural networks that provide high accuracy was conducted, and VGG16, MobileNet, and NASNetMobile neural networks have been selected for research. Transfer learning and fine-tuning has been applied to the given neural networks to adapt the networks for the task of skin lesion classification. It is established that the use of these techniques allows to obtain high accuracy of the neural network for this task. The process of converting a convolutional neural network to an optimized Flatbuffer format using TensorFlow Lite for placement and use on a mobile device is described. The performance characteristics of the selected neural networks on the mobile device are evaluated according to the classification time on the CPU and GPU and the amount of memory occupied by the file of a single network is compared. The neural network file size was compared before and after conversion. It has been shown that the use of the TensorFlow Lite converter significantly reduces the file size of the neural network without affecting its accuracy by using an optimized format. The results of the study indicate a high speed of application and compactness of networks on the device, and the use of graphical acceleration can significantly decrease the image classification time of the tumor. According to the analyzed parameters, NASNetMobile was selected as the optimal neural network to be used in the mobile application of melanoma detection.