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.

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

A REVIEW ON COMPUTER AIDED DETECTION AND DIAGNOSIS OF LUNG CANCER NODULES

2012 ◽  
Vol 3 (3) ◽  
pp. 393-400 ◽  
Author(s):  
S.Shaik Parveen ◽  
Dr. C Kavitha
Keyword(s):  
Lung Cancer ◽  
Medical Information ◽  
Imaging Techniques ◽  
Computer Aided Detection ◽  
Ultrasound Diagnostics ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Diagnosis Of Lung Cancer ◽  
Short Time ◽  
Early Detection And Diagnosis

In this paper, a attempt has been made to summarize some of the information about CAD and CADx for the purpose of early detection and diagnosis of lung cancer. Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx), are procedures in medical information that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. Thus, the use of digital computers to assist practitioners and physicians in diagnosing diseases and to offer a rapid access to medical information gained importance. CAD systems help scan digital images, e.g. from Computed Tomography (CT), for typical appearances and to highlight conspicuous sections, such as focal areas of lung nodules.

Pleural Effusion as a Sign of Coronavirus Disease 2019 (COVID-19) Pneumonia: A Case Report

2020 ◽  
Vol 20 ◽  
Author(s):  
Zahra Ahmadinejad ◽  
Faeze Salahshour ◽  
Omid Dadras ◽  
Hesan Rezaei ◽  
SyyedAhmad Alinaghi
Keyword(s):  
Lung Cancer ◽  
Differential Diagnosis ◽  
Pleural Effusion ◽  
Oxygen Saturation ◽  
Ct Scan ◽  
Chest Ct ◽  
Health Concern ◽  
Lung Involvement ◽  
Diagnosis Of Lung Cancer ◽  
Chest Ct Scan

Background: Recently, COVID-19 infection has become a public health concern. On March 12th, 2020, the World Health Organization (WHO) announced it as a global pandemic. Early diagnosis of atypical cases of COVID-19 infection is critical in reducing the transmission and controlling the present pandemic. In the present report, we described a patient with the chief complaints of dyspnea and dry cough referred to the oncology center at Imam Khomeini Hospital, Tehran with the differential diagnosis of lung cancer who was diagnosed and treated for COVID-19 infection in follow up. Case presentation: A 59-year-old patient complained of fever, dry cough, and dyspnea from two weeks ago. The patient had been referred to this center with the differential diagnosis of lung cancer due to the massive pleural effusion in initial chest CT scan. Dyspnea was the patient’s main complaint at the time of admission in this center and the oxygen saturation was 84%. In the new chest CT scan, similar findings were observed. Due to the severe respiratory distress, a chest tube was placed in the chest cavity to remove the pleural effusion fluid on day one. The patient’s felt relieved immediately after the procedure; however, the oxygen saturation did not raise above 85% despite the oxygen therapy. The cytology of pleural fluid was negative for malignant cells. On day 2, the lymphopenia and high level of CRP suggested the COVID-19 infection. Therefore, a control chest CT scan was conducted and the test for COVID-19 was performed. The CT report indicated the clear pattern of COVID-19’s lung involvement in the absence of pleural effusion. Thus, the treatment for COVID-19 was immediately initiated. On day 4, the test reported positive for COVID-19. Conclusion: Currently, it is important to bear in mind the COVID-19 infection in evaluating the patients with respiratory symptoms. This report indicated how misleading the presentation of chest CT scan could be in clinical judgment. Therefore, we recommend ruling out the COVID-19 infection in all the patients with any pattern of lung involvement to avoid missing the potential cases of this vicious infection.

scholarly journals Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Saleem Iqbal ◽  
Khalid Iqbal ◽  
Fahim Arif ◽  
Arslan Shaukat ◽  
Aasia Khanum
Keyword(s):  
Lung Cancer ◽  
Ct Scan ◽  
Imaging Modality ◽  
Image Database ◽  
Computer Aided Diagnosis ◽  
Features Extraction ◽  
Lung Nodules ◽  
Computer Aided ◽  
Diagnosis Of Lung Cancer ◽  
Aided Diagnosis

Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of “Lung Image Database Consortium-Image Database Resource Initiative” taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible.

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