Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images

Abstract

Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms—bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)—was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.

Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors

“…Therefore, we speculate that the wavelet transform and Gaussian filter can highlight the details in the original images and show more information, and thus can better reflect the heterogeneity between tumors, which is consistent with the conclusions of Qi et al and Zhao et al 32,37 GLCM is a type of image analysis technology that can describe the distribution and shape of image pixels in the form of a grey matrix. 38 GLRLM is the same as GLCM, which can evaluate the distributions of discrete grayscale in the images; however, GLCM evaluates gray symbiosis between adjacent pixels or voxels, and GLRLM evaluates the run length. 39 The definition and calculation method of the GLSZM is based on the GLRLM, and can be used to calculate the number of groups (or regions) that connect voxels.…”

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

“…In 2022, one study presented GLCM feature extraction, fuzzy c-means and KNN (Knearest neighbour) techniques to aid lung cancer CAD systems. However, they did not achieve better output performance and presented output performance measures around 90.9% accuracy [14].…”

“…The profound work to implement potential FE and FS techniques may increase the efficiency of lung cancer assessment. Some recent studies proposed a variety of FS techniques such as local and global feature extraction, GLCM (grey level co-occurrence matrix) based features and handcrafted features to achieve better accuracy; however, they have specific limitations such as the use of access computation power and obtaining less relevant output performance [13,14].…”

Application of fractal and textural analysis in medical physiology, pathophysiology and pathology

“…Textural indicators are computed based on higher mathematical operations and the second order statistical analysis is often required (16)(17)(18)(19). Gray level co-occurrence matrix approach is often used for this although today there are many different alternatives.…”

“…Entropy depends on the level of chaos and disorder of texture, while the contrast is an indirect quantification of textural heterogeneity. Most textural features are mathematically interrelated although the strength of correlation may vary due to many contributing factors (16)(17)(18)(19).…”