Lung Cancer Detection using 3D Convolutional Neural Networks

Pradhan, Adarsh; Sarma, Bhaskarjyoti; Dey, Bhiman Kr

doi:10.1109/compe49325.2020.9200176

Cited by 16 publications

(6 citation statements)

References 16 publications

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“…The proposed GA-NN with SVM method is compared with existing approaches as indicated in (4,6,10,14) respectively.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

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Lung Cancer Detection by Multiple Feature Subset Extraction and Selection based on SVM-Weights and Genetic Algorithm-Neural Network

Ashwini¹,

Kurian²,

Chidanandamurthy³

et al. 2023

IJST

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Objectives: To develop an optimum hybrid approach for lung cancer detection by multiple feature subset extraction and selection based on SVM-weights and Genetic Algorithm (GA-NN) in order to improve the performance measures such as accuracy, sensitivity and specificity. Methods: Initially in preprocessing phase, Computed Tomography (CT) lung images are de-noised using median filter and enhanced using contrast stretching. In the next phase, candidate patch extraction is formed and Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) features are extracted. This is followed by feature selection using Genetic Algorithm-Neural Network (GA-NN) with SVM weights. Finally, images are classified as cancerous and non-cancerous using multiple classifiers (SVM and KNN). For this research work, CT lung images are collected form LIDC dataset. Around 500 images are used out of which 70% is used for training and 30% is used for testing. Findings: From simulation results and comparative analysis, it is observed that GANN with SVM weights result in better predictive performance metrics with notable improvements. The suggested feature subset reduction outperforms current techniques for detection of lung cancer in CT images. The proposed method has resulted in improved accuracy, specificity and sensitivity by 95.8%, 91.3% and 93.5% respectively which is higher than the existing approaches. Novelty: This work presents a novel approach to detect the lung cancer by multiple feature subset extraction and selection based on SVM-Weights and Genetic Algorithm -Neural Network (GA-NN) with improved accuracy, sensitivity and specificity.

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“…The proposed GA-NN with SVM method is compared with existing approaches as indicated in (4,6,10,14) respectively.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

“…Currently cancer classification is based on subjective interpretation of histopathological and clinical data. Clinical information may be inadequate at times and the wide classes of most tumors lack morphologic features which are indispensable for classification (2)(3)(4) .…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Detection by Multiple Feature Subset Extraction and Selection based on SVM-Weights and Genetic Algorithm-Neural Network

Ashwini¹,

Kurian²,

Chidanandamurthy³

et al. 2023

IJST

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“…Only the examined lung tissue is considered a candidate area for lung detection of malignant nodules. The abnormal region may be used for feature vectors which are classified using a fuzzy neural classifier and the calculated area [21][22][23][24][25].…”

Section: Literature Surveymentioning

confidence: 99%

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

Blessie¹,

Ramesh²

2023

Computer Systems Science and Engineering

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The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field. In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields. This helps to show better results especially for diagnosing diseases. Of late the early prediction of cancer is necessary to prevent disease-causing problems. This work is proposed to identify lung cancer using lung computed tomography (CT) scan images. It helps to identify cancer cells' affected areas. In the present work, the original input image from Lung Image Database Consortium (LIDC) typically suffers from noise problems. To overcome this, the Gabor filter used for image processing is highly enhanced. In the next stage, the Spherical Iterative Refinement Clustering (SIRC) algorithm identifies cancer-suspected areas on the CT scan image. This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages. These new methods help to remove unwanted portions of the CT image and better utilization the image. The subspace extraction of features approach is beneficial for evaluating lung cancer. This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures (CCPNN). By using the feature values from the fourth step of the procedure, the proposed CCPNN tends to categorize the lesion in the lung nodular site. The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall (%), precision (%), F-measure (percent), and accuracy (%). Finally, the incorrect classification ratios are determined to compare the trained networks' effectiveness, through these parameters of CCPNN, it obtains the outstanding performance of 98.06% and it has provided the lowest false ratio of 1.8%.

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“…Many studies have explored methods of classifying lung cancer by 3D-CNN based on CT scan images. Pradhan et al [43] applied a series of morphological processes to remove the lung nodules mask and introduce the detected objects to the 3D-CNN model. Khumancha et al [44] applied cubic masks to specify the region of interest (ROI) in CT scans and presented the predicted regions to 3D-CNN for lung cancer detection.…”

Section: Related Studiesmentioning

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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Lung Cancer Detection using 3D Convolutional Neural Networks

Cited by 16 publications

References 16 publications

Lung Cancer Detection by Multiple Feature Subset Extraction and Selection based on SVM-Weights and Genetic Algorithm-Neural Network

Lung Cancer Detection by Multiple Feature Subset Extraction and Selection based on SVM-Weights and Genetic Algorithm-Neural Network

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

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

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