An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

Main Article Content

Kavita Singh, Usha Chauhan, Lokesh Varshney, A.R. Verma

Abstract

Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging.  These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy.

Article Details

How to Cite
Kavita Singh, et al. (2023). An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms . International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 729–741. https://doi.org/10.17762/ijritcc.v11i10.8569
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Articles
Author Biography

Kavita Singh, Usha Chauhan, Lokesh Varshney, A.R. Verma

Kavita Singh1 , Usha Chauhan2 , Lokesh Varshney3 , A.R. Verma4

1Department of Electrical, Electronics and Communication Engineering

 Galgotias University,

Uttar Pradesh, India

 kavitasingh78@gmail.com

Orcid Id: 0000-0002-9728-7460

2Corresponding Author: usha.chauhan@galgotiasuniversity.edu.in

        2Department of Electrical, Electronics and Communication Engineering

 Galgotias University,

Uttar Pradesh, India

Orcid Id:  0000-0003-1359-4082

3Department of Electrical, Electronics and Communication Engineering

 Galgotias University,

Uttar Pradesh, India

lokesh.varshney@gmail.com

Orcid Id: 0000-0001-8305-1687

 4Department of Electronics and Communication Engineering

 G.B.Pant Institute of Engineering and Technology,

Uttarakhand, India

arverma06ei03@gmail.com

Orcid Id :0000-0003-3139-7103