scholarly journals Lung Cancer Detection using CT SCAN Images

2021 ◽  
pp. 611-633
Author(s):  
Dr. M. V. Karthikeyan ◽  
Aravindh. S ◽  
P. Laxman Guru Vignesh
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Imaging Modality ◽  
Lung Nodule ◽  
Computational Cost ◽  
Pulmonary Nodules ◽  
Detection Performance ◽  
Lung Segmentation ◽  
Wide Range ◽  
Lung Cancer Detection

Detection of pulmonary nodules has a crucial effect on the diagnosis of lung cancer, but the detection is a nontrivial task, not only because the appearance of pulmonary nodules varies in a wide range, but also because nodule densities have low contrast against adjacent vessel segments and other lung tissues. Computed tomography (CT) has been shown as the most popular imaging modality for nodule detection, because it has the ability to provide reliable image textures for the detection of small nodules. The development of lung nodule CADe systems using CT imaging modality has made good progress over the past decade. Generally, such CADe systems consist of three stages: 1) image pre-processing, 2) initial nodule candidates (INCs) identification, and 3) false positive (FP) reduction of the INCs with preservation of the true positives (TPs). In the pre-processing stage, the system aims to largely reduce the search space to the lungs, where a segmentation of the lungs from the entire chest volume is usually required. Because of the high image contrast between lung fields and the surrounding body tissue, image intensity-based simple thresholding is effective, and is currently the most commonly used technique for lung segmentation. This paper proposes an adaptive solution to mitigate the difficulty of thresholding-based method in lung segmentation. Sufficient detection power for nodule candidates is inevitably accompanied by many (obvious) FPs. A rule-based filtering operation is often employed to cheaply and drastically reduce the number of obvious FPs, so that their influence on the computationally more expensive learning process can be eliminated. In general, FP reduction using machine learning has been extensively studied in the literature. Compared with unsupervised learning that aims to find hidden structures in unlabelled data, supervised learning, which aims to infer a function from labelled training data, is more frequently used to design a CADe system. Compared with the existing approaches, the morphology based lung cancer detection can be an alternative with either comparable detection performance and less computational cost, or comparable cost and better detection performance.

scholarly journals “ EARLY PREDICTION OF LUNG CANCER DETECTION USING EXTENDED CONVOLUTIONAL NEURAL NETWORKS”

2021 ◽  
Vol 58 (1) ◽  
pp. 5614-5624
Author(s):  
Dr. Asadi Srinivasulu, Dr. Umesh Neelakantan, Tarkeshwar Barua
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cancer Detection ◽  
Computational Cost ◽  
Malignant Growth ◽  
Huge Number ◽  
Threat Level ◽  
Precision Time ◽  
Lung Cancer Detection

Lung disease is one of the significant reasons for malignancy related passing because of its forceful nature and postponed discoveries at cutting edge stages. Early discovery of disease would encourage in sparing a huge number of lives over the globe consistently. Lung malignant growth discovery at beginning time has gotten significant and furthermore simple with picture handling and profound learning systems. Lung Cancer side effects are persistent cough, chest torment that deteriorates with profound breathing, roughness, unexplained loss of hunger and weight, coughing up blood or rust-shaded mucus, brevity of breath, bronchitis, pneumonia or different diseases that continue repeating. Lung quiet Computer Tomography (CT) check pictures are utilized to identify and arrange the lung knobs and to recognize the threat level of that knob. Extended Convolutional Neural Networks (ECNN) work achieved relative examination with parameters like precision, time intricacy and elite, lessens computational cost, and works with modest quantity of preparing information is superior to the current framework. consumers.

IoT based lung cancer detection using machine learning and cuckoo search optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkatesh Chapala ◽  
Polaiah Bojja
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Lung Nodule ◽  
Cuckoo Search ◽  
High Accuracy ◽  
Ct Images ◽  
Lung Nodules ◽  
Content Type ◽  
Otsu Thresholding ◽  
Lung Cancer Detection

Purpose Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers. Design/methodology/approach IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious. Findings The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer. Practical implications The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis. Originality/value The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.

Lung Nodule Classification on CT Images Using Deep Convolutional Neural Network Based on Geometric Feature Extraction

2020 ◽  
Vol 10 (9) ◽  
pp. 2042-2052
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
ChoonSung Nam ◽  
Dong Ryeol Shin
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Ct Images ◽  
Geometric Feature ◽  
Test Accuracy ◽  
Non Gaussian ◽  
Lung Cancer Detection

Lung cancer detection in the earlier stage is essential to improve the survival rate of the cancer patient. Computed Tomography [CT] is a first and preferred modality of imaging for detecting cancer with an enhanced rate of diagnosis accuracy owing to its function as a single scan process. Visual inspections of the CT images are prone to error, as it is more complex to distinguish lung nodules from the background tissues which are subjective to intra and interobserver variability. Hence, computer-aided diagnosis is essential to support radiologists for accurate lung nodule prediction. To overcome this issue, we propose a deep learning approach for automatic lung cancer detection from a low dose CT images. We also propose image pre-processing using Efficient Adaptive Histogram Equalization based Region of Interest [EAHE-ROI] to enhance the CT scan and to eliminate artefacts which occur due to noise and variations of the image. The ROI is extracted from CT scans using morphological operators, thus reducing the number of false positives. We chose geometric features as they extract more geometric elements like curves, lines and points of cancer nodules. Our Non-Gaussian Convolutional Neural Networks [NG-CNN] architecture contains feature extractor and classifier, which has been applied on training, validation and test dataset. Our proposed methodology offers better-classified outcome and effectual cancer detection by outperforming the other competing methods and gives a test accuracy of 94.97% and AUC 0.896.

scholarly journals Development of CAD System for Automatic Lung Nodule Detection: A Review

2021 ◽  
Vol 41 ◽  
pp. 04001
Author(s):  
Sekar Sari ◽  
Indah Soesanti ◽  
Noor Akhmad Setiawan
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
False Positive ◽  
Lung Nodule ◽  
Cad Systems ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Lung Cancer Detection

Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection.

Green synthesis of nanomaterials for early lung cancer detection

2018 ◽  
pp. 1-1
Keyword(s):  
Lung Cancer ◽  
Green Synthesis ◽  
Cancer Detection ◽  
World Wide ◽  
Early Stage ◽  
Age Groups ◽  
Early Lung Cancer ◽  
Lung Cancer Detection ◽  
Synthesis Of Nanomaterials

Lung cancer is the foremost cause of cancer-related deaths world-wide [1]. It affects 100,000 Americans of the smoking population every year of all age groups, particularly those above 50 years of the smoking population [2]. In India, 51,000 lung cancer deaths were reported in 2012, which include 41,000 men and 10,000 women [3]. It is the leading cause of cancer deaths in men; however, in women, it ranked ninth among all cancerous deaths [4]. It is possible to detect the lung cancer at a very early stage, providing a much higher chance of survival for the patients.

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