Open Access

ARTICLE

Detecting Lung Cancer Using Machine Learning Techniques

by Ashit Kumar Dutta^*

Department of Computer Science and Information System, College of Applied Sciences, Almaarefa University, Riyadh, 11597, Kingdom of Saudi Arabia

* Corresponding Author: Ashit Kumar Dutta. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 1007-1023. https://doi.org/10.32604/iasc.2022.019778

Received 25 April 2021; Accepted 24 June 2021; Issue published 22 September 2021

Abstract

In recent days, Internet of Things (IoT) based image classification technique in the healthcare services is becoming a familiar concept that supports the process of detecting cancers with Computer Tomography (CT) images. Lung cancer is one of the perilous diseases that increases the mortality rate exponentially. IoT based image classifiers have the ability to detect cancer at an early stage and increases the life span of a patient. It supports oncologist to monitor and evaluate the health condition of a patient. Also, it can decipher cancer risk marker and act upon them. The process of feature extraction and selection from CT images plays a key role in identifying cancer hot spots. Convolutional Neural Network (CNN) is one of the efficient feature extraction techniques that improves the performance of image classifier by reducing the entropy of image data sets. A Random Forest (RF) classifier is a machine learning technique that can improve its efficiency with the support of CNN. This paper presents an RF classifier with CNN based technique to improve the percentage of accuracy in detecting cancer hot spots with CT images. The experimentation of the proposed approach is based on three dimensions: Feature extraction, selection, and prediction of cancer hot spots. To evaluate the performance of the proposed approach, benchmark image repositories which consists of 3954 images and 50 low dose whole lungs CT scan images are employed. The proposed method achieves an effective result on all test images under different aspects. Consequently, it obtains an average accuracy of 93.25% and an F-measure of 91.75% which is higher than the other methods, comparatively.

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