Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

Jena, Sanjukta Rani; George, S. Thomas

doi:10.1002/ima.22445

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…In the era of digital medicine, the use of artificial intelligence has resulted in good performance for predicting image-related tasks, specifically the use of convolutional neural networks (CNNs). In lung cancer research, CNNs have been applied to LDCT and chest radiographic images to facilitate detection and classification of pulmonary nodules; these models demonstrate performance that is comparable to that achieved by human experts [15][16][17][18][19]. The prediction performance is largely based on high-level feature extraction and nonlinear prediction via the use of CNNs.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

et al. 2021

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Background Artificial intelligence approaches can integrate complex features and can be used to predict a patient’s risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. Objective The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. Methods We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. Results The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease. Conclusions Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

et al. 2021

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“…Authors applied contextual attention to improve deep features and spatial attention was used to locate Region of interest (ROIs) and an ensemble was applied to enhance the robustness of detection method. Jena et al [18] designed a Kernel based non-gaussian CNN (KNG-CNN) model for proficient lung cancer classification. The model consists of three convolutions with three pooling and two fully connected layers.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

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“…A total of 120 neurons make up the first Fully Connected layer (FC). Overfitting may be avoided with the aid of the dropout layer [19]. The CT images are sent into the trained CNN after being segmented into 32× 32 patches with each voxel as the centre point.…”

Section: Page 526mentioning

confidence: 99%

Lung Cancer Segmentation and Classification Using Integration of Convolutional Neural Network & Unet Network Over CT Images: A Deep Learning Approach

Mathews

2022

IJMTS

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Purpose: Cellular breakdown in the lungs screening is a cycle that is utilized to recognize the presence of cancer in the lungs in any case. Mostly it occurs among the elderly, especially smokers. Lung infections are lung-affecting illnesses that impede the respiratory mechanism. Cellulose breakdown in the lungs is one of the top causes of mortality in people all over the globe. Early recognition can improve endurance chances. As the world is revolutionizing with so many emerging technologies and one of the most popular technology is Deep Learning (DL) which has shown tremendous development in medical fields. So this paper brings an effective deep learning framework for lung cancer detection. Objective: To develop a CAD system for efficient lung cancer detection from CT images using a combination of CNN and UNET. For improving accuracy in the proposed system a better feature extraction and feature selection techniques is incorporate, i.e., by using autoencoder and selection based on Kernal function for effective boostings. Methodology: For this deep learning framework, the following are the stages. (a) Data Collection from the popular repository IQ-OTHNCCD lung cancer dataset which contains CT images of a total of 1198 from 110 CT slice cases, (b) Preprocessing CT images with an alpha-trimmed mean filter and CLAHE for improved enhancement, (c) Segmentation using CNN for segmenting the cancer regions) with the use of an autoencoder, extracting characteristics like area, perimeter, centroid, and mean intensity) feature selection using kernel function and finally f) classification using UNet network. Findings/Result: In terms of accuracy, sensitivity, specificity, recall, precision, F1-score, detection rate, TPR, FPR, and computation time, experiments are carried out on a range of cutting-edge models, and our suggested model surpasses them all (accuracy:0.95, sensitivity:0.97, specificity:0.98, detection rate:0.94). Originality: This paper is incorporating 2 neural networks over main stages such as segmentation and classification which eventually improves the quality of the model higher and also these are performed over real-time public medical records which shows the novelty of the model. Paper type: Methodology paper

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Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

Cited by 14 publications

References 24 publications

Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Lung Cancer Segmentation and Classification Using Integration of Convolutional Neural Network & Unet Network Over CT Images: A Deep Learning Approach

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