An Artificial Neural Networks (ANN) Based Lung Nodule Identification and Verification Module

Sandhiya, S.; Kalpana, Y.

doi:10.5958/0974-1283.2019.00039.2

Cited by 3 publications

(4 citation statements)

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“…Morphological reconstruction filtering can be regarded as a kind of nonlinear filtering. Unlike traditional nonlinear filtering, it is not simply a window function convolution operation based on the local characteristics of the image, but is divided into two processes: selection and reconstruction [ 10 , 11 ]. The key to using morphological reconstruction filtering to extract lung parenchyma lies in how to generate suitable labeled images and construct corresponding filters.…”

Section: Design Of Auxiliary Diagnosis System For Chest Ct Assessment Of Pulmonary Nodulesmentioning

confidence: 99%

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

Wang

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

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Section: Design Of Auxiliary Diagnosis System For Chest Ct Assessment Of Pulmonary Nodulesmentioning

confidence: 99%

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

Wang

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The portioned lungs' textural accents were taken off, and it was provided. The neurological system is used to differentiate between the various lung diseases [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Classification and Prediction Using Machine Learning and Image Processing

Nageswaran

Arunkumar²,

Bisht

et al. 2022

BioMed Research International

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Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K -means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.

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“…When it comes to diagnosing lung cancer, Palani et al [12] had put a premium on the application of image processing techniques. The investigation of lung cancer is getting the deep learning treatment.…”

Section: Literature Surveymentioning

confidence: 99%

“…Clinical variables include age, gender, timing of specimen collection, family history of lung cancer, smoking history, and more; imaging measurements typically include nodule shape, nodule type, nodule region, nodule count and nodular boundary in MRI scans [11]. However, because to their subjective nature and lack of standardisation, these characteristics rarely provide a sufficient complete characterization of malignant lesion appearances [12].…”

Section: Introductionmentioning

confidence: 99%

Independent Weighted Feature Set with Linked Feature Reduction Model for Lung Cancer Stage Detection using Machine Learning Model

Kumar,

Rao

2023

IJRITCC

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Lung cancer is a potentially fatal disease that is affected to 18% of population every year. Finding the exact location of a cancer and identification of lung cancer stage continues to be difficult for medical professionals. The true reason for cancer and a comprehensive cure is still unknown. Treatment for cancer is possible if detected at an early stage with accurate stage detection. Finding areas of the lung that have been impacted by cancer requires the use of image processing techniques like noise reduction, highlight filtration, recognizable proof of effected lung regions, and perhaps a comparison with data on the curative history of lung cancer. This research investigates whether or not technology enabled by machine learning algorithms and image processing can correctly classifies and predict lung cancer. For images, the dimensional feature channel is used in the preliminary processing stage. The proposed model considers Magnetic Resonance Imaging (MRI) images for detection of lung cancer. This research proposes an Independent Weighted Feature Set with Linked Feature Reduction (IWFS-LFR) model for accurate lung cancer stage detection based on the size of the tumour. The tumour stage can be accurately predicted using the feature attribute similarity calculation for accurate detection of lung cancer stage for proper diagnosis. The proposed model when contrasted with the traditional model exhibits better performance.

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An Artificial Neural Networks (ANN) Based Lung Nodule Identification and Verification Module

Cited by 3 publications

References 0 publications

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules

Lung Cancer Classification and Prediction Using Machine Learning and Image Processing

Independent Weighted Feature Set with Linked Feature Reduction Model for Lung Cancer Stage Detection using Machine Learning Model

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