Prediction of Lung Cancer using Convolution Neural Networks

Vij, Aanchal; Kaswan, Kuldeep Singh

doi:10.1109/aisc56616.2023.10085058

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…A comprehensive dataset of labelled lung CT images, encompassing both benign and cancerous instances of lung cancer, was compiled by Vij and Kaswan [5]. The provided photos are utilised as input for the training of Convolutional Neural Network (CNN) models, enabling them to acquire the ability to differentiate between several categories of lung nodules that are suggestive of malignancy.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification and Diagnosis of Lung Cancer Based Using CNN with VGG-19

2024

CEIS

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Lung cancer is a major contributor to cancer-related mortality globally, and timely identification is essential for enhancing patient prognosis. Recently, deep learning methods, specifically Convolutional Neural Networks (CNN), have demonstrated encouraging outcomes in image-based medical diagnosis. The paper suggests utilising a CNN-based method to diagnose lung cancer from a healthcare image dataset, with a specific focus on histopathological image data. The proposed CNN approach utilises the natural hierarchical characteristics found in healthcare imagery to autonomously acquire distinctive features that indicate lung cancer. Transfer learning from extensive image datasets and improving models that have been trained are used to tackle the limitations of limited healthcare image datasets successfully. The CNN model utilising the VGG-19 architecture is developed and tested on a comprehensive dataset of lung cancer patients. Following extensive testing and evaluation, the model demonstrates high accuracy as well as precision and recall in diagnosing lung cancer using medical imaging. Interpretability techniques are utilised to get insights into the model's decision-making process, hence increasing its transparency and therapeutic relevance. The proposed CNN-based technology has the potential to help radiologists and clinicians discover and diagnose lung cancer earlier, leading to better patient care and treatment outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

Classification and Diagnosis of Lung Cancer Based Using CNN with VGG-19

2024

CEIS

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“…The versatility of KNN in handling lung cancer prediction is evident across these studies, with its application ranging from analyzing patient characteristics and symptoms to evaluating medical images. Despite the competition from other algorithms like SVM, Randon Forest, and Decision Tree; KNN remains a valuable tool in the arsenal against lung cancer due to its ability to provide satisfactory prediction results with relatively high accuracy and lower false detection rates compared to some other methods (6)(7)(8)(9)(10) . This collective research underscores the importance of continuing to refine and test KNN within the context of lung cancer prediction to harness its full potential in aiding early diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

“…KNN is a staple in the world of machine learning. When the algorithm is used to predict lung cancer risk, if the medical features of a patient are very similar to those of lung cancer patients, then that individual might be predicted to be at a higher risk (9) . The importance of KNN in medical diagnosis can be attributed to its non-parametric nature and ease of understanding.…”

Section: Introductionmentioning

confidence: 99%

Predicting Lung Cancer with K-Nearest Neighbors (KNN): A Computational Approach

Moon,

Jetawat

2024

IJST

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Background: This study introduces a prediction system for lung cancer that uses the K-Nearest Neighbors (KNN) algorithm, one of the most important cancer-related diseases worldwide. Early detection and prediction can significantly improve survival rates. This study is one of the leading causes of cancer death worldwide. Objective: This study aims to develop a prediction system for lung cancer utilizing the K-Nearest Neighbors (KNN) algorithm, addressing the critical need for early detection in combating one of the leading causes of cancer-related mortality worldwide. Method: Leveraging a dataset comprising 10,000 patient records encompassing demographics, medical history, and radiographic features, we conducted preprocessing and normalization before training, validating, and testing the KNN model. Optimal parameter selection facilitated the achievement of a 95% accuracy rate in predicting lung cancer, highlighting the efficacy of KNN in this context. Findings: Our study underscores the potential of machine learning, specifically KNN, in enhancing medical diagnostics. By integrating machine learning techniques into medical practice, we can facilitate early detection and prompt intervention, thereby potentially improving patient outcomes. Novelty: This research contributes to the ongoing integration of machine learning into medical diagnostics, particularly in the realm of cancer prediction. Our findings demonstrate the utility of KNN in accurately predicting lung cancer, thereby offering a promising avenue for enhancing early detection strategies. Result and Discussion: The effectively demonstrate the potential of the K-Nearest Neighbors (KNN) algorithm in achieving a remarkable accuracy rate of 95.0% in predicting lung cancer, as evidenced by rigorous preprocessing and optimization of a dataset comprising 10,000 patient records. The integration of machine learning techniques, exemplified by the KNN algorithm, holds significant promise for improving early detection and subsequent treatment outcomes in lung cancer. By leveraging large datasets and advanced algorithms, we can pave the way for more effective diagnostic tools in combating this devastating disease. Keywords: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Lung cancer detection, Medical imaging, Early diagnosis, Machine learning, Patient outcomes

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ML-based early detection of lung cancer: an integrated and in-depth analytical framework

Sinjanka,

Kaur,

Musa

et al. 2024

Discov Artif Intell

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The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introduces a comprehensive Machine Learning (ML) model designed to predict lung cancer at an early stage, utilizing a dataset sourced from Kaggle. Built on the Random Forest algorithm, the model assesses a diverse set of characteristics and variables, including gender, age, and exposure to various environments and lifestyles. It accurately identifies individuals at a higher risk of developing early-stage lung cancer, facilitating prompt intervention and personalized treatment strategies. Key evaluation metrics demonstrating the model's effectiveness include precision, F1 score, recall, and accuracy. The findings indicate a model accuracy of approximately 97.9%, underscoring its potential as a valuable tool for enhancing the early detection of lung cancer.

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Prediction of Lung Cancer using Convolution Neural Networks

Cited by 6 publications

References 20 publications

Classification and Diagnosis of Lung Cancer Based Using CNN with VGG-19

Classification and Diagnosis of Lung Cancer Based Using CNN with VGG-19

Predicting Lung Cancer with K-Nearest Neighbors (KNN): A Computational Approach

ML-based early detection of lung cancer: an integrated and in-depth analytical framework

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