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 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

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

REVIEW ON LUNG CANCER DETECTION USING IMAGE PROCESSING TECHNIQUE

This paper presents a review on the lung cancer detection method using image processing. In recent years the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages.Also the different procedures and design methodologies for the same have also been discusse

Image processing techniques for Lung Cancer Detection

Lung cancer is one of the dangerous disease which causes cancer deaths in the world. A cancer is an abnormal growth of cells that can be typically derived from a single abnormal cell. Cancerous cells can increase and affect whole part of the lungs. So, it is important to find cancerous cells at the earlier stage and take necessary steps to cure. Now-a-days Magnetic Resonance Imaging and computed tomography (CT) are finding the application computer aided diagnosis and treatment planning. In this paper we use CT scan images. A Computed Tomography(CT) scan of the lung nodule is one of the sensitive method for detecting lung cancer. In this paper proposed different automated nodule recognition systems using image segmentation, feature extraction and processing

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

AUTOMATED COMMUNICATION SYSTEM FOR DETECTION OF LUNG CANCER USING CATASTROPHE FEATURES

Jedan od najvećih izazova s kojima se svijet danas suočava je smrtnost od raka. Jedan od četiri svih dijagnosticiranih karcinoma uključuje karcinom pluća, gdje je smrtnost visoka, čak i nakon tolikog tehničkog i medicinskog napretka. Većina slučajeva raka pluća dijagnosticira se u trećem ili četvrtom stadiju, kada se bolest ne može liječiti. Glavni razlog najveće smrtnosti zbog karcinoma pluća je nedostupnost sustava za „preskrining“ koji može detektirati stanice raka u ranim fazama. Stoga je potrebno razviti sustav za predklinički pregled koji pomaže liječnicima da pronađu i otkriju rak pluća u ranim fazama. Od svih vrsta karcinoma pluća, adenokarcinom se povećava alarmantnom brzinom. Razlog se uglavnom pripisuje povećanoj stopi pušenja - i aktivnom i pasivnom. U ovom radu razvijen je sustav za klasifikaciju plućnih žljezdanih stanica za rano otkrivanje raka korištenjem više prostora u boji. Za segmentaciju koriste se razne tehnike klasteriranja na različitim prostorima boja kao što su HSV, CIELAB, CIEXYy i CIELUV. Značajke se izdvajaju i klasificiraju pomoću Support Vector Machine (SVM).One of the biggest challenges the world face today is the mortality due to Cancer. One in four of all diagnosed cancers involve the lung cancer, where the mortality rate is high, even after so much of technical and medical advances. Most lung cancer cases are diagnosed either in the third or fourth stage, when the disease is not treatable. The main reason for the highest mortality, due to lung cancer is because of non availability of prescreening system which can analyze the cancer cells at early stages. So it is necessary to develop a prescreening system which helps doctors to find and detect lung cancer at early stages. Out of all various types of lung cancers, adenocarcinoma is increasing at an alarming rate. The reason is mainly attributed to the increased rate of smoking - both active and passive. In the present work, a system for the classification of lung glandular cells for early detection of Cancer using multiple color spaces is developed. For segmentation, various clustering techniques like K-Means clustering and Fuzzy C-Means clustering on various Color spaces such as HSV, CIELAB, CIEXYy and CIELUV are used. Features are Extracted and classified using Support Vector Machine (SVM)

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

Lung Cancer Detection using Supervised Machine Learning Techniques

In recent times, Lung cancer is the most common cause of mortality in both men and women around the world. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. The possibility of a patient's survival rate increasing if lung cancer is identified early. To detect and diagnose lung cancer in its early stages, a variety of data analysis and machine learning techniques have been applied. In this paper, we applied supervised machine learning algorithms like SVM (Support vector machine), ANN (Artificial neural networks), MLR (Multiple linear regression), and RF (random forest), to detect the early stages of lung tumors. The main purpose of this study is to examine the success of machine learning algorithms in detecting lung cancer at an early stage. When compared to all other supervised machine learning algorithms, the Random forest model produces a high result, with a 99.99% accuracy rat