Improving the diagnosis of pancreatic cancer based on image processing and machine learning techniques

سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 521

فایل این مقاله در 10 صفحه با فرمت PDF قابل دریافت می باشد

این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

ECMCONF01_097

تاریخ نمایه سازی: 5 آبان 1397

چکیده مقاله:

In the current age, pancreatic cancer is one of the worst forms of cancer. The complications of pancreatic include five types of pancreatitis, benign tumors, malignant tumors, benign cysts and malignant cysts. This cancer has a few clinical symptoms than other cancers. Also, if not treated in a timely manner, it also causes other organs of the body and the patient chance of survival is greatly reduced. One of the ways to detect this disease is to use CT scan images. But the appearance of pancreatic complications is very different in a similar category, and their tissue is very similar to healthy abdominal tissues. For this reason, it s very difficult to identify the range of complications. Materials and Methods: In this study, the data contained 151CT scan images. These images are divided into five classes of pancreatitis, malignant tumors, benign tumors, malignant cysts, benign cysts and a healthy class. The pancreatic complications are varied and different, if the diagnostic system is based on simple experts; the possibility of achieving high detection accuracy is not possible. According to the results of this study, lonely no classification can detect all diseases and combining these methods is the best option. Therefore, in this study we have achieved high accuracy in prediction (690. 69) by combining the perception, convolution and SVM neural networks.

نویسندگان

Tayyebeh Mohammadi

MSc Student, Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran

Saeed Ayat

Associate Professor, Department of Computer Engineering and Information Technology, Payame Noor University, Iran