Lung cancer detection using image processing and deep learning

Al-Ameer, Asraa A. Abd; Hussien, Ghufran Abdulameer; Al-Ameri, Hajer Adnan

doi:10.11591/ijeecs.v28.i2.pp987-993

Cited by 10 publications

(8 citation statements)

References 18 publications

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“…A DNN is a multi-layered representation of a complex data correlation. By automating the extraction of hierarchical features and complicated patterns from input data, DNNs have radically changed ML [21], [22]. Both ML and DL could benefit from ensemble learning [23].…”

Section: Methodologies Employedmentioning

confidence: 99%

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Sahoo,

Nayak,

Tripathy

et al. 2024

IJEECS

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Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.

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Section: Methodologies Employedmentioning

confidence: 99%

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Sahoo,

Nayak,

Tripathy

et al. 2024

IJEECS

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“…− Evaluate the performance of new models: it is essential to evaluate the performance of the newly developed machine learning models for predicting lung cancer risk by comparing them to existing models [39], [40]. This comparison should consider various critical factors such as precision, interpretability, algorithmic effectiveness, and usability [41]. By analyzing these aspects, we can determine the strengths and weaknesses of each model, identify areas for improvement, and ensure that the new models are reliable and effective tools for predicting an individual's risk of developing lung cancer.…”

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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“…Our understanding of cancer's molecular characteristics has advanced thanks to recent studies. This results in more productive computational methods [22,23].…”

Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Ghanashyam Sahoo

2023

IJRITCC

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Predicting progression and deciding on the best follow-up techniques for breast cancer patients is difficult because the illness is diverse and characterized by varying relapse risks. Due to its prevalence, breast cancer has become the top cause of mortality among women worldwide, making diagnosis and prognosis particularly challenging areas of medical study. In addition, the fear of a cancer relapse is a major factor influencing cancer patients' quality of life. The study aims to help doctors determine the likelihood of a breast cancer relapse by applying ensemble learning techniques. In this research, artificial neural networks (ANN) and deep neural networks (DNN) ensembled with Weighted averaging, minority, and majority voting approaches have been investigated for performance enhancements on the breast cancer recurrence dataset sourced from the UCI-ML repository. The empirical analysis shows that this ensemble learning-enabled proposed novel approach shows improved accuracy, precision, sensitivity, specificity, and F1-score of 96.21%, 96.59%, 98.84%, 84.62%, and 97.41%, respectively. The findings of this study can aid doctors in making more informed treatment decisions, thereby improving patient outcomes.

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Lung cancer detection using image processing and deep learning

Cited by 10 publications

References 18 publications

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Predicting lung cancer risk using explainable artificial intelligence

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

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