SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans

Çifçi, Mehmet Akif

doi:10.1155/2022/1139587

Cited by 10 publications

(3 citation statements)

References 46 publications

(35 reference statements)

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“…Similarly, Nasser and Naser [ 27 ] detected lung cancer using an artificial neural network (ANN) with a high accuracy of 96.67%. Cifci et al [ 28 ], the suggested Deep Learning with Instantaneously Trained Neural Networks (DITNN) method, coupled with the enhanced profuse clustering technique (IPCT), managed to improve lung image quality and the diagnosis rate of lung cancer with 98.42% accuracy. With an accuracy performance of 0.909 and 0.872, respectively, the paper in [ 29 ] suggested a double convolutional deep neural network (CDNN) and a conventional CDNN to recognize and classify lung nodules.…”

Section: Computer-assisted Lung Cancer Detection Using Ct Imagesmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

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One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

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Section: Computer-assisted Lung Cancer Detection Using Ct Imagesmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

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“…In this study, for the COVID-19 predictions, we used the available data for research purposes online [36][37][38][39][40][41]. From different countries' data from the date 11 March 2020-29 March 2020, we used different Python modules to visualize and describe the data and then trained ML time series models with 80% of data and tested on 20% of data.…”

Section: Design Of the Predictive Models And Experimental Setupmentioning

confidence: 99%

Machine Learning-Driven Approach for a COVID-19 Warning System

et al. 2022

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The emergency of the pandemic and the absence of treatment have motivated researchers in all the fields to deal with the pandemic situation. In the field of computer science, major contributions include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Since the emergence of information technology, data science and machine learning have become the most widely used techniques to detect, diagnose, and predict the positive cases of COVID-19. This paper presents the prediction of confirmed cases of COVID-19 and its mortality rate and then a COVID-19 warning system is proposed based on the machine learning time series model. We have used the date and country-wise confirmed, detected, recovered, and death cases features for training of the model based on the COVID-19 dataset. Finally, we compared the performance of time series models on the current study dataset, and we observed that PROPHET and Auto-Regressive (AR) models predicted the COVID-19 positive cases with a low error rate. Moreover, death cases are positively correlated with the confirmed detected cases, mainly based on different regions’ populations. The proposed forecasting system, driven by machine learning approaches, will help the health departments of underdeveloped countries to monitor the deaths and confirm detected cases of COVID-19. It will also help make futuristic decisions on testing and developing more health facilities, mostly to avoid spreading diseases.

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“…Therefore, early-stage detection can lead to better treatment for the patient and avert the spread of cancer at the right time, leading to an increase in the survival rate (6) . Doctors can benefit significantly from a wealth of imaging data to interpret lesions (7) accurately. Multi-parameter Magnetic Resonance Imaging (Mp-MRI), which consists of diffusion-weighted imaging (DWI), KTrans imaging, and dynamic contrast-enhanced imaging (DCE).…”

Section: Introductionmentioning

confidence: 99%

A Novel 3D Multi-Layer Convolutional Neural Networks for Lung Cancer Segmentation in CT Images

Chithra,

Bhavani

2024

IJST

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Background/Objectives: A novel three-dimensional efficient Multi-Layer Convolutional Neural Network (3D-MLCNN) is proposed for detecting lung tumors accurately using Computerized Tomography (CT) lung tumor images. The proposed K-means segmentation algorithm for labeling the tumor region automatically. This proposed K-means segmentation algorithm automatically labels the tumor regions to process with the 3D MLCNN model to predict tiny tumors and extract tumor regions accurately. Methods: The proposed 3DMLCNN network goal is to extract the tumor region in CT lung images to classify the lung tumor volume by pixel-wise segmentation model. Findings: The proposed 3D MLCNN segmentation model for detecting the segmenting tumors produces outperforming results for predicting even tiny tumors in the lung images. Experimental results demonstrated with lung cancer CT images in TCIA datasets show that the proposed model 3D-MLCNN achieved a dice coefficient (9.6%), Intersection over Union (IoU) (80%), F1-Score (9.33%), Sensitivity (17.11%), and Accuracy (98%) respectively. However, the proposed model 3D MLCNN was evaluated and compared with the existing state-of-the-art segmentation methods, which shows a 10% improvement in the segmentation process. Novelty: A novel 3D MLCNN model enhances the tumor region and predicts the tumor accurately by labeling the tumor using K-means labeling techniques. Keywords: 3D Convolutional Neural Networks, Lung Cancer CT Images, K - Means Labeling, Feature Visualization, Deep Learning

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SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans

Cited by 10 publications

References 46 publications

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

Machine Learning-Driven Approach for a COVID-19 Warning System

A Novel 3D Multi-Layer Convolutional Neural Networks for Lung Cancer Segmentation in CT Images

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