Pyramid Xception network for accurate detection of abdominal abnormalities in CT scans

Abdominal abnormalities are deviations from the normal structure or function of organs and tissues within the abdominal cavity. Effective detection and diagnosis of these abnormalities, such as tumors, infections, cysts, structural anomalies, and inflammations, are essential for proper treatment and management. Abnormalities can arise from various factors, including disease, injury, and congenital conditions, making accurate detection crucial for timely medical intervention. Hence, this paper presents the Pyramid Xception Network (PyX-Net) framework, an advanced method for detecting abdominal abnormalities using Computed Tomography images. The methodology starts with acquiring abdominal CT images from a medical database, followed by image enhancement through Anisotropic Diffusion. Organ segmentation is then performed using a Conditional Generative Adversarial Network. After segmenting the organs, features are extracted using the Gray-Level Co-occurrence Matrix and wavelet texture analysis. The final detection of abnormalities is achieved with the PyX-Net framework, which integrates Pyramid Network (PyramidNet) and the Xception model. PyX-Net achieved an accuracy of 91.269%, a True Positive Rate of 92.237%, and a True Negative Rate of 91.142% with a K-group of 9.