Early Lung Cancer Detection by Using Artificial Intelligence System

Lung cancer is by far the primary cause of cancer deaths globally. Computer-aided diagnosis (CAD) system is used for the prediction of lung cancer which helps to attain a high detection rate and reduces the time consumed for analyzing the sample. In this paper, CAD system based on sputum color images is proposed which consists of four main processing steps. It starts with the preprocessing step using a heuristic rule-based and a Bayesian classification method using the histogram analysis. In this step, the region of interest (ROI) representing the sputum cell is detected and extracted. In order to segment the nuclei from the cytoplasm, mean shift segmentation is used. The next step is feature analysis. Finally, the diagnosis is done using a rule-based algorithm alongside the artificial neural network (ANN) and support vector machine (SVM) for identifying cancerous and non-cancerous cells. The performance evaluation was done based on the sensitivity, specificity, and accuracy. Our methods are validating by using a set of experiments conducted with a data set of 100 images. The final results showed that the techniques used outperformed conventional methods. The proposed CAD system achieved a reasonable accuracy above 95% with high true positive rates that can basically meet the requirement of clinical diagnosis

Detection Technique of Squamous Epithelial Cells in Sputum Slide Images Using Image Processing Analysis

A good quality sputum is important to detect diseases. The presence of squamous epithelial cells (SEC) in sputum slide images is important to determine the quality of sputum. The presence of overlapping SEC in sputum slide images causes the process become complicated and tedious. Therefore this paper discusses on technique of detection and summation for Squamous Epithelial Cell (SEC) in sputum slide image. We addressed the detection problem by combining K-means and color thresholding algorithm. The design of aided system is evaluated using 200 images and the proposed technique is capable to detect and count each SEC from overlapping SEC image. Total of 200 images were clustered to 10 groups, labelled as Group Cell 1 to group Cell 10 that correspond to the number of cells in the image. Therefore, each group will contain 20 images. The accuracy of the algorithm to detect SEC was also measured, and results show that in 91% which provides a correct SEC detection and summation

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

Detection Technique of Squamous Epithelial Cells in Sputum Slide Images using Image Processing Analysis

A good quality sputum is important to detect diseases. The presence of squamous epithelial cells (SEC) in sputum slide images is important to determine the quality of sputum. The presence of overlapping SEC in sputum slide images causes the process become complicated and tedious. Therefore this paper discusses on technique of detection and summation for Squamous Epithelial Cell (SEC) in sputum slide image. We addressed the detection problem by combining K-means and color thresholding algorithm. The design of aided system is evaluated using 200 images and the proposed technique is capable to detect and count each SEC from overlapping SEC image. Total of 200 images were clustered to 10 groups, labelled as Group Cell 1 to group Cell 10 that correspond to the number of cells in the image. Therefore, each group will contain 20 images. The accuracy of the algorithm to detect SEC was also measured, and results show that in 91% which provides a correct SEC detection and summation

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

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

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits

Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%

Fluorescence Bronchoscopic Surveillance in Patients With a History of Non-Small Cell Lung Cancer

Background Second lung primaries occur at a rate of 2% per patient per year after curative resection for non-small cell lung carcinoma (NSCLC). The aim of this study was to evaluate the role of fluorescence bronchoscopy using the Xillix® LIFE-Lung Fluorescent Endoscopy SystemTM (LIFE-Lung system) in the surveillance of patients for second NSCLC primaries after resection or curative photodynamic therapy (PDT)

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