A Survey on Convolutional Neural Network (Deep-Learning Technique) -Based Lung Cancer Detection

Gumma, Lakshmi Narayana; Thiruvengatanadhan, R.; Kurakula, LakshmiNadh; Sivaprakasam, T.

doi:10.1007/s42979-021-00887-z

Cited by 12 publications

(3 citation statements)

References 38 publications

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“…From the literature review [22][23][24], it is obvious that most of the digital image processing techniques utilized in this field have both benefits and limitations, which are illustrated in below:…”

Section: Related Workmentioning

confidence: 99%

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Rani S,

et al. 2023

ijcsm

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Lung cancer is one of the most serious and prevalent cancers in the globe. Early detection of lung cancer can increase a patient's chances of life. Computed Tomography (CT) scan images are difficult for clinicians to utilize in order to determine the stages of cancer. Computer-aided systems can assist researchers in more precisely predicting the stages of lung cancer in recent times. This study demonstrates the use of technology that is made possible by machine learning and image processing to accurately classify and predict the lung cancer from CT images. The existing tumor detection frameworks have the major difficulties in terms of high complexity, overfitting and error prediction. Therefore, the proposed work aims to formulate a simple and accurate automated system for the prediction and classification of lung cancer from CT images. Before classifying the input lung scan image, an adaptive median filtering approach is used to improve its contrast and quality. From the segmented lung parts, the histogram and texture features are derived. The most relevant characteristics are chosen using the Lion-Butterfly Optimization (LBO) method for training and testing operations. Eventually, the input CT picture is correctly predicted as either healthy or disease-affected using the Stacking Ensemble Learning Classification (SELC) algorithm. In this study, a thorough performance evaluation is conducted utilizing several measures in order to analyze the outcomes

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Section: Related Workmentioning

confidence: 99%

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Rani S,

et al. 2023

ijcsm

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“…The weighted average of intensities based on the considered thresholds (cumulative probability) is obtained through Eq (6). In the next step, the average intensity in all classes for the whole image is obtained through Eq (7).…”

Section: Plos Onementioning

confidence: 99%

“…Most methods for automatic lung cancer detection and classification combine image processing techniques with deep learning and machine learning methods. Many methods have been proposed, including support vector machines (SVM) [ 7 ], artificial neural networks (ANN) [ 8 ], random forests [ 9 ], genetic algorithms (GA) [ 10 ], fuzzy C-means clustering [ 11 ], and deep learning-based features fusion [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Deep fusion of gray level co-occurrence matrices for lung nodule classification

2022

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Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.

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Advanced Machine Learning Techniques for Precise Lung Cancer Detection from CT Scans

Dhanwanth,

Vivek,

Shobana

et al. 2024

Lecture Notes in Networks and Systems

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A Survey on Convolutional Neural Network (Deep-Learning Technique) -Based Lung Cancer Detection

Cited by 12 publications

References 38 publications

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Deep fusion of gray level co-occurrence matrices for lung nodule classification

Advanced Machine Learning Techniques for Precise Lung Cancer Detection from CT Scans

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