Detecting Lung Cancer Using Machine Learning Techniques

Ashit Kumar Dutta

doi:10.32604/iasc.2022.019778

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.

Highlights

The exponential growth of the Internet of Things (IoT) in healthcare services is recently leading to new research ideas in the field of Machine Learning (ML) techniques [1,2]
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
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

Summary

Introduction

The exponential growth of the Internet of Things (IoT) in healthcare services is recently leading to new research ideas in the field of Machine Learning (ML) techniques [1,2]. The diagnosis model based on the IoT technique can be effective in detecting abnormalities at an early stage and provides a safe environment for the patient to recover quickly. Random Forest (RF) is one of the supervised learning algorithms It is a set of decision trees that has the potential to iterate itself for producing optimal results [12]. The RF classifier is a subset of the decision tree, which uses an ensemble learning technique [18,19,20,21] It generates multiple trees depending on the number of data and integrates the outcome of all the trees. The contribution of this study is summarized as follows: The development of IoT based image classification techniques in healthcare services provides an opportunity to increase the survival rate of patients with LC.

Research Background and Related Works

Research Methodology

Results and Discussion

Conclusion