Lung Cancer Detection using Convolutional Neural Network

Sharma, Sayana; Kaur, Mandeep; Saini, Deepak Kumar

doi:10.35940/ijeat.f8836.088619

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…There was a significant amount of data lost in the process of maintaining the class equivalence. The lung cancer detection in CT scan images using CNN is proposed by Sharma, et al in [15]. The researchers have performed preprocessing and segmentation.…”

Section: Literature Studymentioning

confidence: 99%

Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

Siddalingappa¹,

Sekar²

2021

IJACSA

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Detection of anomalies from the medical image dataset improves prognosis by discovering new facts hidden in the data. The present study aims to discuss anomaly detection using autoencoders and convolutional neural networks. The autoencoder identifies the imbalance between normal and abnormal samples. They create learning models flexible and accurate on training data. The problem is addressed in four stages: 1) training: an autoencoder is initialized with the hyperparameters and trained on the lung cancer CT scan images, 2) test: the autoencoder reconstructs the input from the latent space representation with a slight variation from the original data, indicated by a reconstruction error as Mean Squared Error (MSE), 3) evaluate: the MSE value of the training and test dataset are compared. The MSE values of anomalous data are higher than a base threshold, detecting those as anomalies, 4) validate: the efficiency metrics such as accuracy and MSE scores are used at both training and validation phases. The dataset was further classified as benign and malignant. The accuracy reported for outlier detection and the classification task is 98% and 97.2%. Thus, the proposed autoencoder-based anomaly detection could positively isolate anomalies from the CT scan images of lung cancer.

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Section: Literature Studymentioning

confidence: 99%

Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

Siddalingappa¹,

Sekar²

2021

IJACSA

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“…Nasser has devised an Artificial Neural Network (ANN) for discerning the presence or absence of lung cancer within the human body. The outcomes of this endeavor have demonstrated the ANN model's remarkable ability to identify the presence or absence of lung cancer with a notable accuracy rate of 96.67% [7]. Abdullah et al [8], on the other hand, used a publicly available dataset targeting a different lung cancer risk classification.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Artificial Intelligence Based Early Lung Cancer Risk Prediction Using LightGBM

2023

IJANSER

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Lung cancer is the most common cancer in the world and is also the deadliest. Early diagnosis can improve patients' life expectancy and reduce the cost of treatment. The aim of this study is to help physicians and patients by using explainable artificial intelligence methods to early diagnose risk of lung cancer. This study will create an opportunity for physicians to make early diagnosis and treatment strategies for patients. In this study, a machine learning model was developed to predict the risk of lung cancer by using the LightGBM algorithm. Furthermore, the SHAP method is used to explain why and how the model's predictions are made, thus making the AI model reliable. These explanations increase the acceptability and reliability of the predictions made by model, while helping physicians and patients understand the model's decisions. The results obtained show that the developed LightGBM model can predict the risk of lung cancer with a 100% accuracy rate. The model has achieved great results in terms of accuracy rate and sensitivity. In addition, the SHAP analyses explain which features each of the model's predictions is based on, which will increase the confidence of physicians and patients in the decisions of artificial intelligence.

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Artificial intelligence in medical diagnostics: A review from a South African context

et al. 2022

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Lung Cancer Detection using Convolutional Neural Network

Cited by 7 publications

References 8 publications

Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

An Explainable Artificial Intelligence Based Early Lung Cancer Risk Prediction Using LightGBM

Artificial intelligence in medical diagnostics: A review from a South African context

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