Early diagnosis of Lung Cancer with Probability of Malignancy Calculation and Automatic Segmentation of Lung CT scan Images

Computer aided detection system was developed to identify the pulmonary nodules to diagnose the cancer cells. Main aim of this research enables an automated image analysis and malignancy calculation through data and CPU infrastructure. Our proposed algorithm has improvement filter to enhance the imported images and for nodule selection and neural classifier for false reduction. The proposed model is experimented in both internal and external nodules and the obtained results are shown as response characteristics curves.

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Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

Frontiers in Public Health ◽

10.3389/fpubh.2021.671070 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinglun Liang ◽

Guoliang Ye ◽

Jianwen Guo ◽

Qifan Huang ◽

Shaohui Zhang

Keyword(s):

Lung Cancer ◽

Early Stage ◽

Pulmonary Nodules ◽

Ct Images ◽

False Positives ◽

Classification Systems ◽

Cad System ◽

Nodule Detection ◽

Diagnosis Of Lung Cancer ◽

Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

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3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2017.11.008 ◽

2018 ◽

Vol 92 ◽

pp. 64-72 ◽

Cited By ~ 20

Author(s):

Weihang Zhang ◽

Xue Wang ◽

Xuanping Li ◽

Junfeng Chen

Keyword(s):

Detection System ◽

Pulmonary Nodules ◽

Computer Aided Detection ◽

Computer Aided ◽

Feature Based

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P2P-COVID-GAN

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2021100105 ◽

2021 ◽

Vol 17 (4) ◽

pp. 101-118

Author(s):

Nandhini Abirami ◽

Durai Raj Vincent ◽

Seifedine Kadry

Keyword(s):

State Of The Art ◽

Automatic Segmentation ◽

Ct Images ◽

Dice Coefficient ◽

Computed Tomography Images ◽

Proposed Model ◽

Novel Method ◽

Lung Infections ◽

Lung Ct ◽

Ct Slices

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.

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