IMPROVEMENT OF INCEPTIONV3 MODEL CLASSIFICATION PERFORMANCE USING CHEST X-RAY IMAGES

Abstract

In this study, we attempted to confirm whether the InceptionV3 model, which shows excellent performance in lung disease classification using chest X-ray images, is suitable for cardiac disease classification. In addition, we proposed a method for improving classification accuracy by improving the structure of the existing InceptionV3 model. The deep learning model used in this study was a modified version of the fully-connected hierarchical structure of InceptionV3. The proposed InceptionV3 model structure was constructed to differentiate between a normal heart and hypertrophic heart. The data used for model training were trained after data augmentation on 1026 chest X-ray images of patients diagnosed with normal heart and cardiac hypertrophy at Kyungpook National University Hospital. The experiment showed a learning classification accuracy of 99.57% and loss of 1.42% for the original InceptionV3 model. The accuracy and loss of the modified InceptionV3 model were 99.81% and 0.92%, respectively. Its classification performance was evaluated based on precision, recall, and F1 score. For a normal heart, precision, recall and F1 score were 78%, 100% and 88%, respectively. For cardiomegaly, classification accuracy, recall and F1 score were 78%, 100% and 88%, respectively. Conversely, the modified model showed 100% precision, 92% recall and 96% F1 score. For cardiomegaly, classification accuracy, recall rate and F1 score were 95, 100 and 97%, respectively. In conclusion, better classification can be achieved if the chest X-ray images for a normal heart and cardiomegaly are classified using the proposed model. Hence, the reliability of the classification performance gradually increases.