A novel CAD system to automatically detect cancerous lung nodules using wavelet transform and SVM

A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.

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Application of Deep Learning in Lung Cancer Imaging Diagnosis

Journal of Healthcare Engineering ◽

10.1155/2022/6107940 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Wenfa Jiang ◽

Ganhua Zeng ◽

Shuo Wang ◽

Xiaofeng Wu ◽

Chenyang Xu

Keyword(s):

Lung Cancer ◽

Computer Vision ◽

Deep Learning ◽

Malignant Tumors ◽

Lung Nodule ◽

Ct Images ◽

Lung Nodules ◽

X Rays ◽

Imaging Diagnosis ◽

Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

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Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

Radiology and Medical Diagnostic Imaging ◽

10.31487/j.rdi.2020.02.05 ◽

2020 ◽

pp. 1-5

Author(s):

Ammar Chaudhry ◽

William H. Moore

Keyword(s):

Sensitivity And Specificity ◽

Chest Radiography ◽

Predictive Value ◽

Lung Nodule ◽

Chest Radiographs ◽

Lung Nodules ◽

Computer Aided Detection ◽

Cad System ◽

Computer Aided ◽

The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

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A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images

MATEC Web of Conferences ◽

10.1051/matecconf/201823202001 ◽

2018 ◽

Vol 232 ◽

pp. 02001 ◽

Cited By ~ 3

Author(s):

Li Zheng ◽

Yiran Lei

Keyword(s):

Differential Operator ◽

Active Contour ◽

Lung Nodule ◽

Region Growing ◽

Lung Nodules ◽

Lung Nodule Detection ◽

Computed Tomography Images ◽

Nodule Detection ◽

Significant Step ◽

Low Sensitivity

The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.

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Lung Nodule Segmentation Using Mathematical Morphology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.1378 ◽

2011 ◽

Vol 58-60 ◽

pp. 1378-1383

Author(s):

Ming Zhi Qu ◽

Gui Rong Weng

Keyword(s):

Mathematical Morphology ◽

Lung Nodule ◽

Lung Nodules ◽

Computer Aided Detection ◽

Cad System ◽

Multi Scale ◽

Morphological Segmentation ◽

Computer Aided ◽

Ct Technology ◽

Detection And Diagnosis

Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.

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Classification of lung nodules using CT images based on texture features and fractal dimension transformation

Доклады Академии наук ◽

10.31857/s0869-56524855558-563 ◽

2019 ◽

Vol 485 (5) ◽

pp. 558-563

Author(s):

V. F. Kravchenko ◽

V. I. Ponomaryov ◽

V. I. Pustovoit ◽

E. Rendon-Gonzalez

Keyword(s):

Fractal Dimension ◽

Block Diagram ◽

False Positive Rate ◽

A Priori ◽

Lung Nodule ◽

Texture Features ◽

Lung Nodules ◽

Cad System ◽

Positive Rate ◽

Priori Information

A new computer-aided detection (CAD) system for lung nodule detection and selection in computed tomography scans is substantiated and implemented. The method consists of the following stages: preprocessing based on threshold and morphological filtration, the formation of suspicious regions of interest using a priori information, the detection of lung nodules by applying the fractal dimension transformation, the computation of informative texture features for identified lung nodules, and their classification by applying the SVM and AdaBoost algorithms. A physical interpretation of the proposed CAD system is given, and its block diagram is constructed. The simulation results based on the proposed CAD method demonstrate advantages of the new approach in terms of standard criteria, such as sensitivity and the false-positive rate.

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Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst218442 ◽

2021 ◽

pp. 435-439

Author(s):

Rajani Kumari ◽

C. Thanuja ◽

K. Sai Thanvi ◽

K. Lakshmi ◽

U. Lavanya

Keyword(s):

Lung Cancer ◽

Neural Networks ◽

Lung Nodule ◽

Region Of Interest ◽

Texture Features ◽

Support Vector ◽

Lung Nodules ◽

Shape Features ◽

Rule Based ◽

Occurrence Matrix

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

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VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Diagnostics ◽

10.3390/diagnostics11122208 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2208

Author(s):

Muhammad Attique Khan ◽

Venkatesan Rajinikanth ◽

Suresh Chandra Satapathy ◽

David Taniar ◽

Jnyana Ranjan Mohanty ◽

...

Keyword(s):

Lung Nodule ◽

Ct Images ◽

Lung Nodules ◽

Detection Accuracy ◽

Learning Framework ◽

Pet Ct ◽

Nodule Mining ◽

Serially Concatenated ◽

Occurrence Matrix

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

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

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-10-2020-0160 ◽

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.

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

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3122 ◽

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.

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