Lung tumour detection and classification using EK-Mean clustering

Sangamithraa, P.B.; Govindaraju, S.

doi:10.1109/wispnet.2016.7566533

Cited by 79 publications

(35 citation statements)

References 2 publications

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“…Textural features are extracted to train the model to classi ication method and classi ied using a support vector machine to recognize cancer stage. The author Sangamithraa and Govindaraju (2016) has proposed that segmentation method is developed through K mean unsupervised learning technique. Pre-processing step is done with median iltering method, which is applied to debug noisy data available in images that improves the accuracy.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of lung carcinoma using computed tomography images

Sagar

Prabhu

2019

ijrps

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Lung carcinoma disease is a serious leading death every day. Early recognition and treatment can save cancer affected the human’s life. Lung carcinoma is the most hazardous and dangerous disease. Computed Tomography scanning images are the most commonly using scanning technology in the health care industry, Computed Tomography scan images are used by doctors to analyze and identify the cancerous lesion present in the lung CT scanned images. The CAD system is a powerful tool for the radiologist to diagnosis the lesion cells in an accurate way. Computer-aided techniques and machine learning techniques use digital image processing methods to implement CAD systems. The prime aim of this investigation and experimentation is to analyze the different CAD techniques, recognizing the best-developed methods and collecting their disadvantages, faults. Analyzing the drawbacks from the previously proposed model, we are implementing new techniques to overcome the drawbacks analyzed with best accuracy results. For developed methods, the feasibility study is carried on each and every step, improvements can be made were listed out. The analysis shows that having a few low accuracies, and some have high accuracy but not having 100% accuracy. In this research, the K-means algorithm is used to segment the images. K-means algorithm is unsupervised learning, which is used when we have an unlabeled dataset, the project aims to increase the accuracy of up to 100%.

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Section: Related Workmentioning

confidence: 99%

Diagnosis of lung carcinoma using computed tomography images

Sagar

Prabhu

2019

ijrps

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“…Sangamithraa and Govindaraju [20] have implemented a k-mean learning algorithm for clustering. During clustering, dataset are grouped according to certain characteristics.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection using Nearest Neighbour Classifier

2019

IJRTE

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One of the most precarious diseases is lung cancer. Lung cancer detection is one of the main challenging dilemma nowadays. Most of the cancer cells are overlies with each other. It is tough to detect the cell but also important to identify the existence of cancer cells in the early stage unless unable to prevent. According to 2018 reports, 17 million new lung cancer cases are identified worldwide. The Computer Tomography can be used for diagnosis of cancer with image processing. In this research, we proposed two steps of process for diagnosing the presence of cancer either benign or malignant. In the first step, features are extracted by using GLCM. In the second step, the lung cancer cells are classified either benign or malignant by using Nearest Neighbour classifier. Experimental results demonstrated that the proposed approach performance is 98.76% classification accuracy for diagnosing the lung cancer data.

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“…Most of the lung cancer disease occur because of tobacco smoke up to the ratio of 85 to 90% and rest 10 to 15% due to other reasons. These cases are now regularly formed by a mix of hereditary components and presentation to asbestos and different types of air contamination [2]. Detecting lung cancer is not an easy task, it is generally handled by doctors and diagnosis is done manually.…”

Section: Introductionmentioning

confidence: 99%

Automatic Lung Cancer Detection using Sobel & Morphological Operations

Soni¹,

Rai²,

Shivhare³

2019

IJEAT

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Cancer is the most dangerous disease that may cause death and lung cancer is one of them which is more common among all. There are various imaging techniques through which organs can be scanned for diagnosis. Lung canceris a disease that may be caused by unrestrained cell growth in lung. Lung canceris the most common and most dangerous cancer. CT scan can obtain the lung images, but still it has been recognized manually. Manual lung cancer detection is a challenging task because false error rate may lead you to compromise with human’s life. There are lots of researches that has been done in this field but still failed to obtain high precision with minimal error rate. Here the system proposes automatic lung cancer detection using Sobel & Morphological operations that can acquire good precision along with cancer area detection. Sobel is a gradient edge detection technique through which absolute gradient magnitude is computed in the reference of 2D input lung image that is later dilated with morphological operator. The obtained result is liable to attain high precision with less false alarm rate.

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Lung tumour detection and classification using EK-Mean clustering

Cited by 79 publications

References 2 publications

Diagnosis of lung carcinoma using computed tomography images

Diagnosis of lung carcinoma using computed tomography images

Lung Cancer Detection using Nearest Neighbour Classifier

Automatic Lung Cancer Detection using Sobel & Morphological Operations

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