Development of Qualitative Model for Detection of Lung Cancer using Optimization

Venkatesh, C.; Bojja, Polaiah

doi:10.35940/ijitee.i8619.078919

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…This study relies on my prior work ( 39 ), in which the outcomes were produced using a Genetic algorithm and particle swarm optimization approaches in addition to LBP and CNN. Cuckoo search optimization is used in this work, together with CNN and LBP, to enhance the accuracy.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

Venkatesh¹,

Ramana

Lakkisetty³

et al. 2022

Front. Public Health

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One of the most common causes of death from cancer for both women and men is lung cancer. Lung nodules are critical for the screening of cancer and early recognition permits treatment and enhances the rate of rehabilitation in patients. Although a lot of work is being done in this area, an increase in accuracy is still required to swell patient persistence rate. However, traditional systems do not segment cancer cells of different forms accurately and no system attained greater reliability. An effective screening procedure is proposed in this work to not only identify lung cancer lesions rapidly but to increase accuracy. In this procedure, Otsu thresholding segmentation is utilized to accomplish perfect isolation of the selected area, and the cuckoo search algorithm is utilized to define the best characteristics for partitioning cancer nodules. By using a local binary pattern, the relevant features of the lesion are retrieved. The CNN classifier is designed to spot whether a lung lesion is malicious or non-malicious based on the retrieved features. The proposed framework achieves an accuracy of 96.97% percent. The recommended study reveals that accuracy is improved, and the results are compiled using Particle swarm optimization and genetic algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

Venkatesh¹,

Ramana

Lakkisetty³

et al. 2022

Front. Public Health

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“…The development of models that can precisely predict a person's risk of getting lung cancer based on various risk factors, such as age, smoking history, and family history of cancer, may be feasible using XAI [10]. Additionally, by comprehending the AI model's decision-making process, medical professionals can learn important information about the risk factors more closely linked to lung cancer, which can help guide in prevention and treatment plans [11].…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Predicting lung cancer risk using explainable artificial intelligence

Shoukat Makubhai,

Pathak,

Chandre

2024

Bulletin EEI

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Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.

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“…The use of explainable AI can support regulatory compliance in addition to increasing the efficacy of intrusion protection [11] [12]. Strict laws, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union, apply to healthcare organisations with respect to the security and privacy of patient data [13].…”

Section: Introductionmentioning

confidence: 99%

Prevention in Healthcare: An Explainable AI Approach

Makubhai,

Pathak,

Chandre

2023

IJRITCC

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Intrusion prevention is a critical aspect of maintaining the security of healthcare systems, especially in the context of sensitive patient data. Explainable AI can provide a way to improve the effectiveness of intrusion prevention by using machine learning algorithms to detect and prevent security breaches in healthcare systems. This approach not only helps ensure the confidentiality, integrity, and availability of patient data but also supports regulatory compliance. By providing clear and interpretable explanations for its decisions, explainable AI can enable healthcare professionals to understand the reasoning behind the intrusion detection system's alerts and take appropriate action. This paper explores the application of explainable AI for intrusion prevention in healthcare and its potential benefits for maintaining the security of healthcare systems.

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Development of Qualitative Model for Detection of Lung Cancer using Optimization

Cited by 6 publications

References 7 publications

A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

A Neural Network and Optimization Based Lung Cancer Detection System in CT Images

Predicting lung cancer risk using explainable artificial intelligence

Prevention in Healthcare: An Explainable AI Approach

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