An enhanced computer-assisted lung cancer detection method using content based image retrieval and data mining techniques

Muthazhagan, Balaji; Ravi, T.; Rajinigirinath, D.

doi:10.1007/s12652-020-02123-7

Cited by 25 publications

(10 citation statements)

References 8 publications

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“…Cancer causes about one in six deaths every year [1][2] and lung cancer stands at the top of all of this as it is responsible for 1.76 million deaths up to 2016. [1] Early detection of cancer can provide a suitable treatment to not just prolong life but also save a patient's life and hence increase the survival rate. […”

Section: Related Workmentioning

confidence: 99%

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Literature Survey for Lung Cancer Analysis and Prediction

Nagalapuram¹,

Varshashree²,

Singh³

et al. 2022

IJRASET

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Lung cancer is one of the most common and deadly cancers worldwide which can be cured only if it is discovered at an early stage. Lung cancer can be diagnosed using various technologies, including MRI, isotopes, X-rays, and CT. One of the most effective ways to fight cancer is to discover it early enough to significantly improve the patient's chances of survival which can be done by the means of Artificial Intelligence. The proposed approach uses past medical records to determine if the patient has lung cancer. The CT scans are analyzed by a Convolutional Neural Network (CNN) model to determine the stage of cancer. Finally, the suggested model would forecast the patient's estimated medical insurance costs. Machine learning (ML) and Deep Learning (DL) approaches will be used to train and test the models by utilizing open-source datasets. Keywords: Lung Cancer, Computed Tomography (CT) scans, Machine Learning (ML), Convolutional Neural Network (CNN), GoogLeNet.

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Section: Related Workmentioning

confidence: 99%

“…The journal paper [1] published by Muthazhagan B, Ravi T, Rajinigirinath D (2021) states that with the aid of current lung cancer prediction technologies, predicting and detecting lung cancer at an early stage is a difficult challenge. An early lung tumor prediction might extend a person's life by one to five years.…”

Section: ][2][3][4]mentioning

confidence: 99%

Literature Survey for Lung Cancer Analysis and Prediction

Nagalapuram¹,

Varshashree²,

Singh³

et al. 2022

IJRASET

View full text Add to dashboard Cite

show abstract

“…Every year, cancer kills around one in every six people [1][2], with lung cancer topping the list with 1.76 million fatalities in 2016. [1] Early cancer identification can give a proper therapy to not only prolong but even save a person's life, hence increasing the survival rate. [1]- [4].…”

Section: Literature Surveymentioning

confidence: 99%

A Web Application for Lung Cancer Analysis and Detection using Deep Learning Approach

Nagalapuram¹

2022

IJRASET

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Lung cancer is one of the most common and deadly cancers worldwide. One of the most effective ways to fight cancer is to discover it early enough to improve the patient’s chances of survival. The Discovery of lung cancer at an early stage helps in reducing its risk. Various technologies like MRI, isotopes, X-rays, and CT scans are used for diagnosis of lung cancer. The studying of lung nodules helps a doctor to determine if the patient is malignant. These nodules sometimes have a chance of growing undetected by the naked eye. In this project, Lung cancer stage is detected with the help of patient details, symptoms and CT scans by using Machine learning and Deep learning algorithms with open-source datasets. The proposed approach uses Machine learning algorithms to study past medical records and determine if the patient has lung cancer. Deep learning models are used to analyze the CT scans to determine the stage of lung cancer. The major goal of this project is to find nodules as small as 3 mm to detect cancer stage accurately. Finally, the machine learning model calculates the patient’s estimated medical insurance costs. This project is useful for the early detection of lung cancer in individuals and can help them in overcoming these health conditions. The effectiveness of cancer prediction systems helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status

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“…Pneumonia infection is a serious disease of the lungs with a range of possible causes. Bacteria, viruses or fungi can cause Pneumonia infection Wale and Sonawani (2018), Varadharajan et al (2018), Muthazhagan et al (2020). Pneumonia is a serious complication of the new coronavirus .…”

Section: Introductionmentioning

confidence: 99%

Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques

Rajasenbagam¹,

Jeyanthi

Pandian

2021

J Ambient Intell Human Comput

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In this research, A Deep Convolutional Neural Network was proposed to detect Pneumonia infection in the lung using Chest X-ray images. The proposed Deep CNN models were trained with a Pneumonia Chest X-ray Dataset containing 12,000 images of infected and not infected chest X-ray images. The dataset was preprocessed and developed from the Chest X-ray8 dataset. The Content-based image retrieval technique was used to annotate the images in the dataset using Metadata and further contents. The data augmentation techniques were used to increase the number of images in each of class. The basic manipulation techniques and Deep Convolutional Generative Adversarial Network (DCGAN) were used to create the augmented images. The VGG19 network was used to develop the proposed Deep CNN model. The classification accuracy of the proposed Deep CNN model was 99.34 percent in the unseen chest X-ray images. The performance of the proposed deep CNN was compared with state-of-the-art transfer learning techniques such as AlexNet, VGG16Net and InceptionNet. The comparison results show that the classification performance of the proposed Deep CNN model was greater than the other techniques.

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An enhanced computer-assisted lung cancer detection method using content based image retrieval and data mining techniques

Cited by 25 publications

References 8 publications

Literature Survey for Lung Cancer Analysis and Prediction

Literature Survey for Lung Cancer Analysis and Prediction

A Web Application for Lung Cancer Analysis and Detection using Deep Learning Approach

Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques

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