An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence

N Arivazhagan; J Venkatesh; K Somasundaram; K Vijayalakshmi; S Sathiya Priya; M Suresh Thangakrishnan; K Senthamilselvan; B Lakshmi Dhevi; D Vijendra Babu; S Chandragandhi; Fekadu Ashine Chamato

doi:10.1155/2022/1078056

An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence

Evid Based Complement Alternat Med. 2022 Jul 7:2022:1078056. doi: 10.1155/2022/1078056. eCollection 2022.

Authors

Affiliations

¹ Department of Computational Intelligence, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, India.
² Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamilnadu, India.
³ Institute of Information of Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamilnadu, India.
⁴ Department of CSE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India.
⁵ Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, Tamilnadu, India.
⁶ Department of Computer Science and Engineering, Einstein College of Engineering, Tirunelveli 627012, Tamilnadu, India.
⁷ Department of Electronics and communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Ponmar, Chennai, Tamilnadu, India.
⁸ Institute of Artificial Intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamilnadu, India.
⁹ Department of Electronics and Communication Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation, Chennai, Tamilnadu, India.
¹⁰ AP/CSE, JCT College of Engineering and Technology, Pichanur, Tamilnadu, India.
¹¹ Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.

Abstract

In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors' time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.

Publication types

Retracted Publication