论文笔记-医学图像分类

Deep convolutional neural network based medical image classification for disease

author

Samir S. Yadav and Shivajirao M. Jadhav

Introduction

基于CNN的图像分类任务中提升性能的方法：

• 数据增强；
• 迁移学习；
• 胶囊网络.

论文贡献：

• 三种分类器性能的比较
  • 带ORB的SVM；
  • VGG16 和 Inception V3 的迁移学习；
  • 胶囊网络。
• 通过CNN上的迁移学习分析 数据增强、网络复杂度、微调卷积层、和其他防止过拟合方法在胸部X-ray数据集上的有效性。

Literature review

• ORB and SVM application on medical image classification

  • Paredes et al. [21] use small patches of medical images as local features and k-nearest neighbor (k-NN) to classify the categorization of the whole medical image, finally achieving start-of-art accuracy.
  • Parveen and Sathik [22] researched to detect Pneumonia from X-rays. The authors extracted features by discrete wavelet transform (DWT), wavelet frame transform (WFT) moreover, wavelet packet transform (WPT) and used Fuzzy C-means to detect Pneumonia.
  • Caicedo et al. [23] use scale-invariant feature transform (SIFT) as a local feature descriptor and use support vector machines (SVM) classifiers to classify medical images and get state-of-art precision at 67%.
  • Rublee et al. [24] propose a free, faster local feature descriptor-oriented fast and rotated binary robust independent elementary features (ORB), which has the same performance as SIFT and even better performance than SIFT under some condition.

• CNN on medical image classification

  • Qing et al. [28] presented a customized CNN with shallow ConvLayer to classify image patches of lung disease.
  • Kermany et al. [3] use InceptionV3 with ImageNet trained weight and transfer learning on a medical image dataset containing 108,312 optical coherence tomography (OCT) images. They got an average accuracy of 96.6%, with a sensitivity of 97.8% and a specificity of 97.4%.
  • Vianna [30] also studied how to utilize transfer learning to build an X-ray image classification system that is the critical component of a computer-aided-diagnosis system. The authors found a finetuned transfer learning system with data augmentation effectively alleviate overfitting problem and yield a better result than two other models: training from scratch and a transfer learning model with only a retrained last classification layer.

• Capsule neural network on medical image classification

  • Afshar et al. [18] applied CapsNet to classifying brain tumors on Magnetic Resonance Imaging (MRI) images and got 86.56% prediction accuracy with a modified CapsNet that reduces the feature maps from the original 256 to 64.
  • Tomas and Robertas [31] presented a CapsNet based solution to classify four types of breast tissue biopsies from breast cancer histology images. They achieved 87% accuracy with the same high sensitivity.
  • Jimenez-Sanchez et al. [5] evaluated the CapsNet on medical image challenges. The authors selected a CNN with three layers of ConvLayer as the baseline and compared CapsNet’s performance with LeNet and the baseline on four datasets, MNIST, Fashion-MNIST, mitosis detection (TUPAC16) and diabetic retinopathy detection (DIARETDB1), with three conditions: the partial subset of the dataset, the imbalanced subset of the dataset and data augmentation. The final result shows CapsNet performed better than the other two networks in a small, imbalanced dataset.
  • Beşer et al. [32] implemented a sign language recognizing system by CapsNet and achieved 94.2% validation accuracy.