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Classification of normal and abnormal images of lung cancer

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TLDR
GLCM method is used for pre-processing of the snap shots and function extraction system and to test the level of diseases of a patient in its premature stage get to know it is regular or unusual.
Abstract
To find the exact symptoms of lung cancer is difficult, because of the formation of the most cancers tissues, wherein large structure of tissues is intersect in a different way. This problem can be evaluated with the help of digital images. In this strategy images will be examined with basic operation of PCA Algorithm. In this paper, GLCM method is used for pre-processing of the snap shots and function extraction system and to test the level of diseases of a patient in its premature stage get to know it is regular or unusual. With the help of result stage of cancer will be evaluated. With the help of dataset and result survival rate of cancer patient can be estimated. Result is based totally on the precise and wrong arrangement of the patterns of tissues.

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Citations
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Journal ArticleDOI

Deep Learning Based Lung Cancer Detection and Classification

TL;DR: In this paper, a deep neural network for detecting lung cancer from CT images is developed and evaluated, which achieved an accuracy of 90.85% with the adaptive boosting algorithm and CNN.
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A Deep Model for Lung Cancer Type Identification by Densely Connected Convolutional Networks and Adaptive Boosting

TL;DR: This work proposes a deep learning model to identify lung cancer type from CT images for patients in Shandong Provincial Hospital, and can achieve identifying accuracy 89.85%, which performs better than DenseNet without adaboost, ResNet, VGG16 and AlexNet.
Journal ArticleDOI

Ensembled liver cancer detection and classification using CT images.

TL;DR: This study demonstrates automated tumor characterization based on liver CT images and will assist the radiologist in detecting and classifying different types of tumors at a very early stage.
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Multivariate time series classification analysis: State-of-the-art and future challenges

TL;DR: This paper has tried to summarize state-of-the-art methods for MTSC analysis complete with their strengths and weaknesses and also focused on some limitations from previous research for developing M TSC analysis in the future.
References
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Journal ArticleDOI

Two-dimensional PCA: a new approach to appearance-based face representation and recognition

TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
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Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage

TL;DR: This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage and warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLc undergoing CT.
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A generic framework for ontology-based information retrieval and image retrieval in web data

TL;DR: This paper proposes and displays an ontology-based object-attribute-value (O-A-V) information extraction system as a web model that acts as a user dictionary to refine the search keywords in the query for subsequent attempts to improve the standard information retrieval systems.
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