Lung Cancer Detection and Classification based on Image Processing and Statistical Learning

Hasan, Md Rashidul; Al, Kabir, Muntasir

doi:10.48550/arxiv.1911.10654

Cited by 2 publications

(1 citation statement)

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“…There has been a significant increase in the death rate from lung cancer in the last few years [1,4,5]. Healthcare providers and physicians count on different methods to diagnose lung cancers, including X-rays of the chest, CT scans, MRIs, and biopsies [6][7][8][9][10][11][12][13]. Initially, physicians determine the stage of cancer and its exact locations to prepare recovery treatment plans and tell patients what to expect in their path against the disease [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Decision Support System for Lung Cancer Diagnosis

Alsheikhy¹,

Said²,

Shawly³

2023

Computer Systems Science and Engineering

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Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN) tool to perform deep learning and a Random Forest Algorithm (RFA) to help classify the type of cancer present using several extracted features, including histograms and energy. Numerous simulation experiments were conducted on MATLAB, evidencing that this system achieves 98.7% accuracy and over 98% precision and recall. A comparative assessment assessing accuracy, recall, precision, specificity, and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics. This study found that using CNNs and RFAs is highly effective in detecting lung cancer, given the high accuracy, precision, and recall results. These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

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