Lung Cancer Detection Using CT Scan Images: A Review on Various Image Processing Techniques

Thabsheera, A. P. Ayshath; Thasleema, T. M.; Rajesh, R

doi:10.1007/978-981-13-2514-4_34

Cited by 23 publications

(6 citation statements)

References 7 publications

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“…Another, researcher [27], presented a approach for segmentation of images using supervised learning method for prediction of cancer in lung [14]. Research conducted by [28] and [29] utilized rule-based classification integrated with SVM and an intelligent and dynamic Prediction method, Intelligent Fuzzy C-means to detect the tumor respectively. Further, [30] also utilized SVM approach for lung cancer prediction.…”

Section: Related Workmentioning

confidence: 99%

Lung cancer prediction and retrieval using multistage hybrid prediction approach

Jayaraj

Kumar

Nagarajan

et al. 2022

ijhs

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In cancer diagnosis computer-aided Prediction is considered as significant research domain. The generation and processing of lung cancer identification and Prediction is expensive. During past decades, content-based image retrieval (CBIR) technique has been applied in various medical applications. For effective diagnosis of lung cancer radiologist’s requires effective approach for cancer prediction and diagnosis. In this research, for prediction of CT lung images is achieved using hybrid Prediction approach is adopted with integration of logistic regression and Adaboost classifier. Feature extraction and retrieval of lung cancer image applied through optimization technique known as firefly approach for processing several vector features of lung images to derive salient characteristics for achieving best features. Results illustrated that proposed prediction and retrieval approach exhibits significant performance rather than conventional technique. The proposed approach provides classification accuracy of 97% which is significantly higher.

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Section: Related Workmentioning

confidence: 99%

Lung cancer prediction and retrieval using multistage hybrid prediction approach

Jayaraj

Kumar

Nagarajan

et al. 2022

ijhs

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“…Radiologists utilize a variety of diagnostic techniques to detect lung cancer, including magnetic resonance imaging (MRI), computed tomography (CT), chest X-ray, and other methods. However, a CT scan is the most reliable screening approach due to its low noise and robustness in determining tumor size [3]. With the widespread use of CT scanning technology, the need for medical image analysis has risen and causes an increased workload for radiologists [4].…”

Section: Introductionmentioning

confidence: 99%

Development of hybrid convolutional neural network and autoregressive integrated moving average on computed tomography image classification

Yahya

Chin

2023

IJ-AI

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One of the deadliest diseases in humans is lung cancer. Radiologists and experienced doctors spend much more time investigating the pulmonary nodules due to the high similarities between malignant and benign nodules. Recently, the computer-assisted diagnosis (CAD) tool for nodule detection can provide a second opinion for the doctor to diagnose lung cancer. Although machine learning technologies are extensively employed to identify lung cancer, the process of these methods is complex. The numerous researches have sought to automate the diagnosis of pulmonary nodules using convolutional neural networks (CNN) to aid radiologists in the lung screening process. However, CNN still confronts some challenges, including a significant number of false positives and limited performance in detecting lung cancer from computed tomography (CT) images. In this work, we proposed a hybrid of CNN and auto-regressive integrated moving average (ARIMA) for lung nodule classification using CT images to address the classification issue. The proposed hybrid CNN-ARIMA can classify CT images successfully with test accuracy, average sensitivity, average precision, average specificity, average F1-Score, and area under the curve (AUC) of 99.61%, 99.71%, 99.43%, 99.71%, 99.57%, and 1.000, respectively.

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“…The requirements and the constraints to be considered for the image-based diagnosis of various categories of ILD which uses only the support of the CT scan images D [4]. There are many methods identified and used in the detection of the various types of Interstitial Lung Diseases such as lung cancer, IPF [3], [5]. IPF results in gradual fibrosing and mostly shows cases of pneumonia which affects the interstitium of the lungs.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of digital chest images using a modified Sobel edge detection algorithm

Aishwarya

Joseph

2021

IJEECS

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Computed tomography (CT) images are an essential factor in the diagnosing procedure for various diseases affecting the internal organs. Edge detection can be used for the appropriate enhancement of the lung CT scan images for the diagnosis of the various interstitial lung diseases (ILD). In order to solve the issues of edge detection provided by the traditional Sobel operator, the paper proposes a Sobel 12D edge detection algorithm which uses the additional direction templates for the better identification of the edge details. First, the vertical and horizontal directions available in the traditional Sobel operator are extended to few more directions (a total of 12 directions) which enhances the edge extraction ability. Next part, compute the edge detected image using the Sobel 12D, Laplace, Prewitt, Robert’s Cross and Scharr operators for edge detection separately. It is followed by image fusion method which optimizes the edge detection by combining the edge detected images obtained using the Sobel 12D approach and the Laplace operator. The experimental results shows that the proposed algorithms generates a better detection of the edges than the other edge detection operators.

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Lung Cancer Detection Using CT Scan Images: A Review on Various Image Processing Techniques

Cited by 23 publications

References 7 publications

Lung cancer prediction and retrieval using multistage hybrid prediction approach

Lung cancer prediction and retrieval using multistage hybrid prediction approach

Development of hybrid convolutional neural network and autoregressive integrated moving average on computed tomography image classification

Enhancement of digital chest images using a modified Sobel edge detection algorithm

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