Classification of Lung Cancer Stages from CT Scan Images Using Image Processing and k-Nearest Neighbours

2020 ◽  
Author(s):  
Mohd Firdaus Abdullah ◽  
Siti Noraini Sulaiman ◽  
Muhammad Khusairi Osman ◽  
Noor Khairiah A. Karim ◽  
Ibrahim Lutfi Shuaib ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Ct Scan ◽  
Nearest Neighbours

scholarly journals Image processing based detection of lung cancer on CT scan images

2017 ◽  
Vol 893 ◽  
pp. 012063 ◽  
Author(s):  
Bariqi Abdillah ◽  
Alhadi Bustamam ◽  
Devvi Sarwinda
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Ct Scan

Intelligent classification of lung malignancies using deep learning techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priyanka Yadlapalli ◽  
D. Bhavana ◽  
Suryanarayana Gunnam
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Ct Scan ◽  
Transfer Learning ◽  
Confusion Matrix ◽  
Chest Ct ◽  
Lung Nodules ◽  
Content Type ◽  
Chest Ct Scan

PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

scholarly journals Optimal Diagnosis of Lung Cancer using CT Images

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2695-2699
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Morphological Characteristics ◽  
Cell Detection ◽  
Cancer Society ◽  
Prostate Cancers ◽  
Diagnosis Of Lung Cancer ◽  
Time Required

According to the American Cancer Society, lung cancer is the second most widespread cancer and the leading cause of cancer deaths in both men and women. The death rate of lung cancer every year is greater than that of colon, breast, and prostate cancers combined. CT scan is a non-invasive method for diagnosis of any ailment, and can be used to detect lung cancer as well. The proposed project involves cell detection using image processing techniques. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung, imaging techniques are used to accelerate diagnosis. The image processing paired with data analysis techniques helps us diagnose the particular type of cancer by comparing the output of the CT scan to an available database of images. This improves accuracy and reduces the time required for the diagnosis. Features of the image under test are extracted and analysed, and the decision regarding the morphological characteristics of the image are made. This helps us arrive at a decision regarding the nature of the image.

scholarly journals Identification and Classification of Lung Nodules Using Neural Networks

2021 ◽  
Vol 12 (6) ◽  
pp. 1956-1961
Author(s):  
Dr. S. Gnanavel Et al.
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Feature Extraction ◽  
Digital Image Processing ◽  
Digital Image ◽  
Extraction Process ◽  
Health Concern ◽  
Image Processing Techniques ◽  
Processing Techniques

Lung cancer is a serious health concern, which is also one of the major types of cancer that has a profound impact on the overall cancer mortality rates. The detection of lung cancer nodules is quite a challenge as the major challenge is the structure of the cancer nodules; here the cells are imbricated with each other. The prediction and classification of lung cancer is done by applying digital image processing techniques to the acquired input images of the nodules. This methodology also aids early detection which in turns reduces the criticality of the condition and provides scope for early intervention and treatment. The prediction methodology involves extracting several features of the lung cancer cell and then applying pattern-based prediction techniques. In recent times, owing to the fact that the time and execution parameters are very important aspects to detect the abnormality of the fast-spreading cancer cells, digital image processing techniques are being widely deployed. The fundamental factors of this research are the quality of image assessment and the precision of feature extraction. Following our proposed methodology, a clear picture of the region of interest is obtained which acts as a basis for the feature extraction process. Here an overall evaluation of the digital image processing techniques used by previous scholars for the finding and classification of lung cancer nodules have also been emphasised.

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