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
ارسال کننده : مجتبی محمدی
کد IOI مقاله : XHCC-KEGED
لینک مستقیم مقاله : https://isnac.ir/XHCC-KEGED