RETRACTED: Image recognition of CT diagnosis for cholangiocarcinoma treatment based on FPGA processor and neural network

Abstract

Abstract Imaging tests can help doctors identify abnormal internal organs that may indicate cholangiocarcinoma. Diagnostic techniques include Computed Tomography (CT) scan. For selected patients with early-stage bile duct cancer, liver transplantation is a viable treatment option. By optimizing radiotherapy and chemotherapy results before surgery, and tests confirmed by abdominal surgery without metastasis. Biliary tract cancer Cubic Centimeter (CC) is the second most common primary hepatobiliary cancer incidence has been rising. CC imaging characteristics, behavior, and treatment strategies depending on the form and location of the tumor and very different. In the cross-sectional images, the CC may (mass formation, infiltration, and catheter tube circumference) and according to the growth pattern positions (liver, hepatic portal around, extrahepatic/distal) classification. CC poor prognosis, surgery is the only treatment option. Biliary bile duct cancer diagnosis usually involves an emergency from diagnosis to treatment. Fatal cancer is attributable to the portion of the support challenges during treatment therapy. This article provides an overview of the intrahepatic and extrahepatic bile duct, including identifying risk factors, different treatments, symptom relief, and an in-depth understanding of common problems. Such as Neural Networks (NN) and the like have been used to interpret the image and Field-Programmable Gate Array (FPGA) diagnosis of Hepatocellular Carcinoma (HCC) and liver block. NN is a machine learning algorithm similar to the depth of learning. It demonstrated its ability to identify the specific function that can detect pathological lesions. Application of Neural Network to assess liver cancer and liver tumor imaging examination to diagnose cancer and evaluate neural networks' accuracy and performance.