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

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

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

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

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)

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Image Processing-Based Lung Cancer Detection Using Adaptive CNN Mixed Sine Cosine Crow Search Algorithm in Medical Applications

Medical image processing relies heavily on the diagnosis of lung cancer images. It aids doctors in determining the correct diagnosis and management. For many patients, lung cancer ranks among the most deadly diseases. Many lives can be saved if cancerous growth is diagnosed early. Computed Tomography (CT) is a critical diagnostic technique for lung cancer. There was also an issue with finding lung cancer due to the time constraints in using the various diagnostic methods. In this study, an Adaptive CNN Mixed Sine Cosine Crow Search (ACNN-SCCS) strategy is proposed to assess the presence of lung cancer in CT images based on the imaging technique. Accordingly, the presented classification scheme is used to assess these traits and determine whether or not the samples include cancerous cells. To obtain the highest level of accuracy for our research the proposed technique is analyzed and compared to many other approaches, and its performance metrics (detection accuracy, precision, f1-score, recall, and root-mean-squared error) are examined