Determination of ۶۸Ga-DOTA-FAPI and ۱۸FDG PET/CT Imaging inLiver Metastases Patients Diagnosis with Chaotic Deep Neural Network
محل انتشار: اولین کنفرانس هوش مصنوعی و پردازش هوشمند
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 360
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
AISC01_064
تاریخ نمایه سازی: 16 آبان 1401
چکیده مقاله:
Malignant liver cancer is the main causes of death all over the world and its incidencerate is increasing every year. According to World Health Organization (WHO) data, liver cancercauses about ۸۳۰,۰۰۰ deaths which is the third leading reason of cancer deaths in ۲۰۲۰-۲۲. Manystudies represented early detection of liver cancer that can help to improve survival rates. However,the symptoms of liver cancer are not apparent, so most patients with liver cancer in the early stagesare already in the middle and late stages at the time of diagnosis, in other senences, the treatmentoptions for them are limited. These factors make liver cancer have a terrible prognosis. Therefore,proposing a method that can effectively perform early detection and help improve the outcome ofliver cancer treatment is great practical importance. This article uses some filters for noise reductionand then use Chaotic Deep Neural Networks (ChDNN) methods for segmentation which optimizedand tuned by a Genetic Algorithm. We use PET/CT imaging performed with ۶۸Ga-DOTA-FAPI and۱۸FDG to diagnosis liver metastases patients. Obtained results represented exact area of nodulesdetection in images.
کلیدواژه ها:
Liver Cancer ، Image Segmentation ، Chaotic Deep Neural Networks (ChDNN) ، Deep Learning ، Genetic Algorithm
نویسندگان
Monireh Ayari
Department of Computer, Karaj Branch, Islamic Azad University, Karaj, Iran
Bashir Bagheri Nakhjavanlo
Department of Computer and Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
Nima Aberomand
Department of Computer Engineering, Shahr-e-Qods, Branch, Islamic Azad University, Tehran, Iran Department of ComputerScience, the University of Texas at Arlington, Texas, USA