scholarly journals Lung Cancer Prediction and Detection Using Image Processing Mechanisms: An Overview

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Bakhan Tofiq Ahmed
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Vital Role ◽  
Support Vector ◽  
Noise Elimination ◽  
Research Papers ◽  
Initial Stage ◽  
Effective System ◽  
Processing Mechanisms ◽  
Lung Cancer Detection

Nowadays, cancer has counted as a hazardous disease that many people suffered from especially Lung-Cancer. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Image Processing Mechanisms have a vital role in predicting and recognizing both benign and malignant cells with the help of classifier mechanisms such as Decision-Tree (D-Tree), A-NN, Support-Vector-Machine, and Naïve-Bayes classifier which are widely utilized in the biomedical field. These classifiers are available to classify the usual and unusual cells. This study aims to review the most well-known Image Processing Mechanisms for Lung-Cancer Detection and Prediction. Brief information about the main steps of proposing an effective system by using Image Processing stages like Image Acquisition, Pre-processing of the image which includes noise elimination and enhancement, Segmentation, Extracting Feature, and Binarization had been demonstrated. In the literature, several researchers' work had been reviewed. A comparison had been done among various reviewed research papers that proposed various models for recognizing and estimating the Lung-Cancer nodule. The comparison based on the Image Processing Mechanisms, accuracy, and classifier used in each reviewed research paper.

scholarly journals An Improved Lung Cancer Prediction System using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 5059-5063
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Gabor Filter ◽  
Noise Removal ◽  
Processing Technique ◽  
Vital Role ◽  
Image Processing Technique ◽  
Respiratory Problems ◽  
Processing Techniques ◽  
Lung Cancer Detection

Lung cancer is a disease that causes the cells present in the lungs which divide uncontrollably. This uncontrollable division of cells causes tumours which in turn decrease a person’s respiration. Early identification and diagnosis will help people to seek treatment and recover soon. Tumours are an abnormal mass of tissue that results when cells divide more than they should or do not die when they should. Identifying lung cancer in its early stages is very difficult but knowing about its symptoms is quite easy. Symptoms may be similar to those of respiratory problems or infections and sometimes there may be no symptoms at all. In this work mainly deals with the lung cancer detection using image processing techniques were involving all the intermediate stages such as preprocessing stage, noise removal, processing stage, postprocessing stage which finally gives output image after all those stages. Doctors can categorize tumour stage as initial or advanced based on patient CT scan report. The abnormal images are subjected to segmentation (threshold segmentation, watershed transformation) to focus on tumour portion. It mainly deals with image quality and clarity. Gabor filter algorithm plays a vital role for image enhancement in removing noise from an image. The ANN method gives us the best performance as it neglects the background and displays the required portion of an image that we need. This image processing technique is one of the most efficient way of detecting lung cancer.

Lung Cancer Detection using Image Processing Techniques and its Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 1578-1583
Author(s):  
Aishwarya .R
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Support Vector Machine ◽  
Cell Carcinoma ◽  
Mortality Rates ◽  
The Body ◽  
Support Vector ◽  
Lung Tumour ◽  
Image Processing Techniques ◽  
Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

scholarly journals CAD System for Lung Cancer and its Stages Detection using Image Processing Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1612-1615
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
False Positive ◽  
False Negative ◽  
Region Growing ◽  
High Rate ◽  
Support Vector ◽  
Image Region ◽  
Image Processing Techniques ◽  
Processing Techniques

The Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system

scholarly journals Lung Cancer Detection Using Marker Controlled Watershed with SVM

2018 ◽  
Vol 5 (1) ◽  
pp. 24-30
Author(s):  
Fatema Tuj Johora ◽  
Mehdi Hassan Jony ◽  
Md Shakhawat Hossain ◽  
Humayun Kabir Rana
Keyword(s):  
Lung Cancer ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Early Stage ◽  
Support Vector ◽  
Lung Cells ◽  
Science And Engineering ◽  
Efficient Performance ◽  
Occurrence Matrix ◽  
Lung Cancer Detection

Lung cancer is one of the most dangerous diseases and prediction of it, is the most challenging problem nowadays. Most of the cancer cells are overlapped with each other. It is hard to detect the cells but also essential to identify the presence of cancer cells in the early stage. Early detection of lung cancer may reduce the death rate. In this study, we used the Grey Level Co-occurrence Matrix (GLCM) to extract the feature of cancer affected lung image and then Support Vector Machine (SVM) has been used to detect normal and abnormal lung cells after implementing the features. Our experimental evaluation using MATLAB demonstrates the efficient performance of the proposed system and in the result. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 24-30

scholarly journals Lungs Cancer Detection Using Digital Image Processing Techniques: A Review

2019 ◽  
Vol 38 (2) ◽  
pp. 351-360 ◽  
Author(s):  
Mati ullah ◽  
Mehwish Bari ◽  
Adeel Ahmed ◽  
Sajid Naveed
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Computer Vision ◽  
Cancer Detection ◽  
Medical Image ◽  
Vital Role ◽  
Classification Methods ◽  
The People ◽  
Medical Diagnostic ◽  
Processing Techniques

From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.

Sign in / Sign up

Export Citation Format

Share Document