نمایش چکیده فارسی

Methods of analyzing data output from artificial intelligence for cancer diagnosis

Erfan Jamshidian1 Sepehr Enteshari2 Elias Karimzadeh3 Mobina Shokrallahi4 Fatima Amini5 Sayed Ali Nourian Najafabadi6 Mojtaba Mohammadi7

Abstract :
Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X‐ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long‐term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep‐seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low‐cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer.

Keywords : cancer diagnosis medical imaging cancer medicine analysis


زبان مقاله : انگلیسی
محور مقاله : هوش مصنوعی
محل انتشار : سومین کنفرانس بین المللی پژوهش ها و دستاوردهای نو در علوم، مهندسی و فناوری های نوین
آدرس وب سایت کنفرانس : setbconf.com/3rd
کد IOI مقاله : XHCC-KEGED
لینک مستقیم مقاله : https://isnac.ir/XHCC-KEGED
نمایش چکیده فارسی : https://isnac.ir/SA/XHCC-KEGED

سایر مقالات ارائه دهنده
  1. طراحی سیستم دزيمتری محيطی ثابت برای کشف و شناسایی حضور مواد پرتوزا و تشعشات هسته ای در مناطق پرتوی
  2. طرح جدید برای گذر از خطا در مزارع بادی مبتنی بر ژنراتور القایی دوتحریکه با قابلیت بهبود ولتاژ لینک DC
  3. مکانیابی سلسله مراتبی مراکز خدمات پزشکی اضطراری 4 سطحی در شرایط عدم قطعیت با استفاده از الگوریتم ژنتیک
  4. نقش و عملکرد سیستم شمارنده متحرک بدون سرنشین طراحی شده در مدیریت بحران شرایط اضطراری پرتوی
  5. بررسی و تعیین حدود آستانه مداخله، معیارهای سمیت در حوادث مواد شیمیایی و پرتوی
  6. بررسی مهندسی روش های اصول گودبرداری و ایمنی در گود
  7. The role of information technology (IT) for adoption of preventive emerging threats, such as Covid-19
  8. Evaluation and performance of nodelectronic photodiode model BWP34 to the reaction of rays
  9. Design and implementation to collect the output pulses from the SI-22G sensor due to the interaction of the rays
  10. Effects and application of high voltage ionization in heavy particles of air and environment
  11. Detection and identification of environmental pollution with spectral methods (ECD)
  12. Methods of analyzing data output from artificial intelligence for cancer diagnosis
  13. Studies of deep learning methods in artificial intelligence to analyze and identify cancer cells
  14. Studies and investigation of the effects of ultraviolet rays on the eyes
  15. Strategic management of artificial intelligence in defense systems and future weapons