Analysis of CT Scan Images to Predict Lung Cancer Stages Using Image Processing Techniques

Islam, Mahmudul; Mahamud, Al Hasib; Rab, Raqeebir

doi:10.1109/iemcon.2019.8936175

Cited by 22 publications

(7 citation statements)

References 8 publications

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“…As long as datasets are correctly modified, it is feasible to create machine learning algorithms that are capable of producing predictions with a high level of accuracy. Mutiullah, et al [17] presented a comprehensive study on various image processing techniques used for lung tumor identification and classification. Nasser, et al [18] utilized an ANN technique for identifying lung tumor from CT images.…”

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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show abstract

“…Several image processing methods are beneficial to detect micro-calcification and classify the cancer into benign and malignancy with the features of texture and shape. An extensivepublication reviewdemonstrates that traditional method in detection and classification of micro-calcification includes enhancement [8], accompanied by segmentation [9], next extraction of features [10], and finally the classification [11]. The images of CT [12] and magnetic resonance imaging (MRI) are employed in detection of lung cancer.…”

Section: Pattern Recognitionmentioning

confidence: 99%

An Early Prediction of Lung Cancer using CT Scan Images

2021

JCNS

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Lung cancer is a common occurrence type in a population and one amonglethal cancers. Recently, out of several research presented by diverse health agencies; it is obvious that the fatality ratio is rising due todelayeddiagnosis of lung cancer. Hence, an artificial intelligence-based diagnosis is required to find out the onset of lung nodule micro-calcification, which may support the doctors and radiologists to accurately predict it through image processing methods. In this paper, a novel technique is proposed to identify the nodule micro-calcification pattern by using its physical features. The physical features that considered are the reflection coefficients and mass densities of the binned CT image of lung. The physical features measurements reiteratesonce again the existence of malignant nodule. Then, by applying the methods of thresholding and in interpolation of physical features, a three-dimensional (3D) projected image of the region of interest (ROI) is achieved in respect of physical dimensions. Thus, the nodule size is calculated from 3D projection. This concept is used to verify how best in classification with 100 malignant images (the nodule presence) and 10 normal images (the nodule absence). Apart size measurement, the proposed method supports SVM classifier to act for excellentclassification from normal and malignantinput imagesby just using two physical features. The classifier exhibited an accuracy of 98%.

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“…Additionally, radiologists may utilise contrast dye to make tumours or lymph nodes more visible. To effectively discern between benign and malignant tumours, CT scan interpretation requires skill [13]. If cancer is suspected, further diagnostic tests, such as biopsies, may be performed to confirm the diagnosis and guide appropriate treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning

Adhikary,

Dutta,

Dwivedi

2023

Biomed. Phys. Eng. Express

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Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.

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Analysis of CT Scan Images to Predict Lung Cancer Stages Using Image Processing Techniques

Cited by 22 publications

References 8 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

An Early Prediction of Lung Cancer using CT Scan Images

Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning

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