Development of algorithm for identification of maligant growth in cancer using artificial neural network

Pandian, R.; Ravikumar, D.; Kumar, Rajeew

doi:10.11591/ijece.v10i6.pp5709-5713

Cited by 6 publications

(4 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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“…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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“…Recent research has improved our grasp of cancer's molecular features. As a result, computational techniques are more efficient [23], [24].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Enhancing lung cancer disease diagnosis by employing ensemble deep learning approaches

Nath Das,

Panda,

Rautray

2023

IJEECS

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<span>Cancer is a disease that results from the unnatural proliferation of aberrant cells that infest the body’s healthy cells and spread throughout the body. Lung cancer is characterized by an imbalance in the cells of the affected organs, namely the lungs. The prediction of lung cancer at an early stage is very important, particularly in countries that are densely populated and have lower incomes. Clinically conventional approaches, such as blood tests and other types of treatments, are used by specialists. The age of artificial intelligence (AI) has begun, and today, it is feasible to construct a computer-aided diagnostic mechanism with the assistance of machine learning and deep learning algorithms. In this particular piece of research, one deep learning algorithm, an artificial neural network (ANN), has been investigated to determine whether or not lung cancer could be detected at an earlier stage. In addition to conventional ANN, ensemble ANN with weighted averaging and soft and hard voting ensemble techniques are also considered. In order to achieve this effectiveness, the state-of-the-art parameters for the proposed method using ANN are assessed and evaluated using the lung cancer dataset. The empirical analysis shows that hard voting-enabled ANN shows the highest accuracy at 97.47%.</span>

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Development of algorithm for identification of maligant growth in cancer using artificial neural network

Cited by 6 publications

References 18 publications

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Enhancing lung cancer disease diagnosis by employing ensemble deep learning approaches

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