Regional Symposium on Innovation in science and technology

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Combining Machine Learning and Nanobiosensors for Improving Lung Cancer Detection

Authors :

Shakiba Nazemian¹ Soheil Sadr² Ashkan Hajjafari³ Khashayar Hajjafari⁴ Abbas Rahdar⁵ Mahdis Khajehmohammadi⁶ Hassan Borji⁷

1- Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mas 2- Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran 3- Department of Pathobiology, Faculty of Veterinary Specialized Science, Science, and Research Branch, Islamic Azad University, Tehran, Iran 4- Medical Doctor, Shahid Bahonar University of Kerman, Kerman, Iran 5- Department of Physics, University of Zabol, Zabol, Iran 6- Department of Basic Sciences, Faculty of Veterinary Medicine, Baft branch, Islamic Azad University, Baft, Iran 7- Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran

Keywords :

Lung cancer،Nanotechnology،Nanobiosensors،Artificial intelligence

Abstract :

Abstract- Lung cancer is one of the most prevalent types of cancer in the world, and its clinical prognosis and early detection are of great importance. With recent advances in artificial intelligence and nanotechnology, combining machine learning (ML) and nanobiosensors has been considered a novel approach for diagnosing and monitoring lung cancer. Hence, the current review aims to examine the combination of these two technologies for lung cancer diagnosis and explores its potential and challenges. ML can identify patterns associated with lung cancer with its capabilities in processing complex data and simulating predictive models. Nanobiosensors, on the other hand, can detect biological changes at the molecular and cellular levels with high sensitivity. These two technologies allow for more accurate, faster, and non-invasive lung cancer diagnosis. In addition, using these two technologies simultaneously could help identify more advanced changes in the disease, even before clinical symptoms become apparent. In conclusion, combining machine learning and nanobiosensors offers a novel and efficient approach to lung cancer diagnosis that can significantly increase the accuracy of diagnosis and prognosis.

Papers List

List of archived papers

SEROLOGICAL AND MOLECULAR DETECTION OF HIGHLY PATHOGENIC AVIAN INFLUENZA OF LAYER HENS CIRCULATING IN DIYALA GOVERNORATE

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Evaluation of heavy metals pollution in agricultural Soil of Chenaran area in NE-Iran

Neda Sharafi - Mohamad Hosein Mahmudy Gharaie - Ali Makhdoumi - Hossein Ahmadzadeh

ارائه روش سریع بازسازی واپیچشی فاز با استفاده از مدل برجستگی در تصاویر رادار روزنه مصنوعی

هومن افشاری راد - سید علیرضا سیدین - حمید حاجی رحیمی کاشانی

A new insight of skin organoids

Milad Rezaei - Fatemeh Jameie - Roya Lari

Synergistic effects of silane-modified carbon black and nano-silica on filler dispersion and dynamic properties of butyl rubber nano compounds

Farzaneh Jaberi Mofrad - Ali Ahmadpour - Saeed Ostad Movahed

Iranian bentonite: characteristics, challenges, and opportunities

Zahra Alaminia - Hossein Sadeghi - Mohammad Vahidinia

Cerium Oxide (CeO2) Nanoparticles for Effective Adsorption of Chromium (III) Ions from the Tannery Effluent Wastewater.

Mohammed Jaafar Ali Alatabe

Monte Carlo modelling of Siemens Artiste linac head and the 160 MLC and assessment of dose maps for IMRT and 3D-CRT plans

Hashem Miri-Hakimabad - Elie Hoseinian-Azghadi - Laleh Rafat-Motavalli - Taylan Tuğrul - Niloofar Rafat-Motavalli - Vida Khodabandeh-Baygi - Mahdieh Dayyani

Application of enhanced Raman scattering (SERS) and advanced statistical methods in veterinary diagnostics

Tina Yaghoobpour - Hassan Sharifiyazdi - Ehsan Rakhshandehroo - Amin Safaie

Dosimetric impact of inter-fraction changes of OARs during intensity-modulated radiation treatment of prostate cancer patients

Mahdieh Dayyani - Laleh Rafat-Motavalli - Elie Hoseinian-Azghadi - Niloofar Rafat-Motavalli - Hashem Miri-Hakimabad