Pulmonary image classification based on inception-v3 transfer learning model

Abstract

Chest X-ray film is the most widely used and common method of clinical examination for pulmonary nodules. However, the number of radiologists obviously cannot keep up with this outburst due to the sharp increase in the number of pulmonary diseases, which increases the rate of missed diagnosis and misdiagnosis. The method based on deep learning is the most appropriate way to deal with such problems so far. The main research in this paper was using inception-v3 transfer learning model to classify pulmonary images, and finally to get a practical and feasible computer-aided diagnostic model. The computer-aided diagnostic model could improve the accuracy and rapidity of doctors in the diagnosis of thoracic diseases. In this experiment, we augmented the data of pulmonary images, then used the fine-tuned Inception-v3 model based on transfer learning to extract features automatically, and used different classifiers (Softmax, Logistic, SVM) to classify the pulmonary images. Finally, it was compared with various models based on the original Deep Convolution Neural Network (DCNN) model. The experiment proved that the experiment based on transfer learning was meaningful for pulmonary image classification. The highest sensitivity and specificity are 95.41% and 80.09% respectively in the experiment, and the better pulmonary image classification performance was obtained than other methods.