Image Visualization and Classification Using Hydatid Cyst Images with an Explainable Hybrid Model

Abstract

Hydatid cysts are most commonly found in the liver, but they can also occur in other body parts such as the lungs, kidneys, bones, and brain. The growth of these cysts occurs through the division and proliferation of cells over time. Cysts usually grow slowly, and symptoms are initially absent. Symptoms often vary in size, location, and the affected organ. Common symptoms include abdominal pain, vomiting, nausea, shortness of breath, and foul odor. Early diagnosis and treatment are of great importance in this process. Therefore, computer-aided systems can be used for early diagnosis. In addition, it is very important that these cysts can be interpreted more easily by the specialist and that the error is minimized. Therefore, in this study, data visualization was performed using Grad-CAM and LIME methods for easier interpretation of hydatid cyst images via a reanalysis of data. In addition, feature extraction was performed with the MobileNetV2 architecture using the original, Grad-CAM, and LIME applied data for the grading of hydatid cyst CT images. The feature maps obtained from these three methods were combined to increase the performance of the proposed method. Then, the Kruskal method was used to reduce the size of the combined feature map. In this way, the size of the 2416 × 3000 feature map was reduced to 2416 × 700. The accuracy of the proposed model in classifying hydatid cyst images is 94%.

Keywords

• artificial intelligence
• CNN
• deep learning
• hydatid cyst
• machine learning