Early detection of lung cancer - A challenge

Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance and shape feature descriptors, which helps in the early diagnosis of lung cancer. 3D Histogram of Oriented Gradient (HOG), Resolved Ambiguity Local Binary Pattern (RALBP) and Higher Order Markov Gibbs Random Field (MGRF) are the feature descriptors used to explain the nodule’s appearance and compared their performance. Lung cancer screening methods, image processing techniques and nodule classification using radiomic-based framework are discussed in this paper which proves to be very effective in lung cancer prediction. Good performance is shown by using RALBP descriptor

Mass Segmentation Techniques For Lung Cancer CT Images

Mass segmentation methods are commonly used nowadays in modern diagnostic centers and research centers working in the field of lung cancer detection and diagnosis. We have implemented k-means and fuzzy cluster means (FCM) techniques for mass segmentation of lung CT images. The methods were compared in terms of area, perimeter and diameter. FCM outperforms K-means in terms of better detection of lung cancer area and effective values of dimensional features of lung cancer as compared to K-means method

The detection and summation of squamous epithelial cells for sputum quality testing

Sputum is mucus that coughs up from the lower airways, which consists of cells such as squamous epithelial cells (SEC), pus cells, macrophages and other cells. SEC that found in sputum is an epithelium characterized by its most superficial layer consisting of flat cells, known as skin cells. Sputum with good quality is important to detect diseases. The quality of sputum is determined using Bartlett‟s Criteria by considering the score of SEC, pus cell (neutrophils) and macroscopy. If the total score is 1 and above, the sputum will be cultured and the specimens will be proceed accordingly. Whereas if the total score is 0 and below, the process of sputum will stop. For squamous epithelial cells, the score is 0 if SEC is less than 10. Whereas if SEC is between 10 to 25, the score is -1 and the score is -2 if the number of SEC is greater than 25. Currently, the detection of SEC in sputum is manually done by technologists. However, the problems if the human do are time consuming and human constraint. So, another method is needed which is by automated vision system using image processing technique in. Image processing such as image segmentation is used to detect and count the number of SEC. Then, the result of SEC is displayed using graphical user interface (GUI). The advantage of GUI is to make computer operation more intuitive and thus easier to use. In conclusion, squamous epithelial cells can be detected using image processing and the score of SEC is determined. Lastly, the percentage of error for this project is calculated

The Value of Autofluorescence Bronchoscopy Combined with White Light Bronchoscopy Compared with White Light Alone in the Diagnosis of Intraepithelial Neoplasia and Invasive Lung Cancer: A Meta-Analysis

ObjectiveTo compare the accuracy of autofluorescence bronchoscopy (AFB) combined with white light bronchoscopy (WLB) versus WLB alone in the diagnosis of lung cancer.MethodsThe Ovid, PubMed, and Google Scholar databases from January 1990 to October 2010 were searched. Two reviewers independently assessed the quality of the trials and extracted data. The relative risk for sensitivity and specificity on a per-lesion basis of AFB + WLB versus WLB alone to detect intraepithelial neoplasia and invasive cancer were pooled by Review Manager.ResultsTwenty-one studies involving 3266 patients were ultimately analyzed. The pool relative sensitivity on a per-lesion basis of AFB + WLB versus WLB alone to detect intraepithelial neoplasia and invasive cancer was 2.04 (95% confidence interval [CI] 1.72–2.42) and 1.15 (95% CI 1.05–1.26), respectively. The pool relative specificity on a per-lesion basis of AFB + WLB versus WLB alone was 0.65 (95% CI 0.59–0.73).ConclusionsAlthough the specificity of AFB + WLB is lower than WLB alone, AFB + WLB seems to significantly improve the sensitivity to detect intraepithelial neoplasia. However, this advantage over WLB alone seems much less in detecting invasive lung cancer

Using Various Processing Methods to Identify Lung Cancer

Lung cancer has become the leading killer of humans stricken with invasive cancer, affecting husbands to wives, friends to neighbors as it is air bourne and causing suffering for many families. Early detection of cancer is one of the important step in treat the decease. According to author prevention is better than cure, basede on that image processing methods are widely used in recent times, for earlier detection and treatment stages. The objective of this work is study of lung cancer images that are collected form different hospitals where the treatment is going on and focuses on early stage lung cancer detection. Lung cancer is prominent cancer that states large number of deaths of more than a million in every year. It makes sense that need of detecting the lung lymph nodule at early stage in computer tomography medical images to detect the occurrence of cancer at early stages, the requirement of procedures, methods and techniques are increasing. There are number of methods and techniques available but none of them provide a better accuracy of detection. One of the techniques is content based image retrieval computer aided diagnosis system (CAD) for early detection of lung nodules from the chest computer tomography (CT) images. The optimization of algorithm allows doctors to identify the lumps present in the CT lung images in the early stage

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image’s threshold limit, the tumor’s area is specified and then edge detection filters are used for detection of the tumor’s edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database

Identification of lung cancer with high sensitivity and specificity by blood testing

<p>Abstract</p> <p>Background</p> <p>Lung cancer is a very frequent and lethal tumor with an identifiable risk population. Cytological analysis and chest X-ray failed to reduce mortality, and CT screenings are still controversially discussed. Recent studies provided first evidence for the potential usefulness of autoantigens as markers for lung cancer.</p> <p>Methods</p> <p>We used extended panels of arrayed antigens and determined autoantibody signatures of sera from patients with different kinds of lung cancer, different common non-tumor lung pathologies, and controls without any lung disease by a newly developed computer aided image analysis procedure. The resulting signatures were classified using linear kernel Support Vector Machines and 10-fold cross-validation.</p> <p>Results</p> <p>The novel approach allowed for discriminating lung cancer patients from controls without any lung disease with a specificity of 97.0%, a sensitivity of 97.9%, and an accuracy of 97.6%. The classification of stage IA/IB tumors and controls yielded a specificity of 97.6%, a sensitivity of 75.9%, and an accuracy of 92.9%. The discrimination of lung cancer patients from patients with non-tumor lung pathologies reached an accuracy of 88.5%.</p> <p>Conclusion</p> <p>We were able to separate lung cancer patients from subjects without any lung disease with high accuracy. Furthermore, lung cancer patients could be seprated from patients with other non-tumor lung diseases. These results provide clear evidence that blood-based tests open new avenues for the early diagnosis of lung cancer.</p

Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer

Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table