Lung cancer prediction model using ensemble learning techniques and a systematic review analysis

Mamun, Muntasir; Farjana, Afia; Mamun, Miraz Al; Ahammed, Md Salim

doi:10.1109/aiiot54504.2022.9817326

Cited by 39 publications

(7 citation statements)

References 18 publications

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“…Mamun et al, 10 Dritsas et al 32 and Vieira et al 33 were selected as the related works for comparison with our proposed methods. We chose them to ensure a fair comparison, as they employ similar ML models and the same dataset as ours.…”

Section: Resultsmentioning

confidence: 99%

“…Mamun et al 10 explored ensemble learning methods, including XGB and light gradient boosting machines (LightGBM), for lung cancer classification, employing an oversampling technique, SMOTE, for enhanced results. This study introduces new ensemble learning models developed based on a survey dataset of 309 individuals with or without lung cancer.…”

Section: Related Workmentioning

confidence: 99%

“…The extensive datasets used in the research mentioned above, ranging from the Kaggle dataset to the Lanzhou University dataset, highlight the diverse sources for obtaining reliable and comprehensive data for enhancing lung cancer prediction and classification. 10 , 12 , 19 , 20 , 31 , 34 , 36 – 38 …”

Section: Related Workmentioning

confidence: 99%

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Optimizing lung cancer classification through hyperparameter tuning

Nabeel,

Bazai,

Alasbali

et al. 2024

DIGITAL HEALTH

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Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Optimizing lung cancer classification through hyperparameter tuning

Nabeel,

Bazai,

Alasbali

et al. 2024

DIGITAL HEALTH

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“…Consequently, the training is repeated for disease prediction. The eventual ensemble approach will improve classification accuracy and performance greatly by making use of the learning capabilities of the base classifier and meta-classifier [28,29]. The structure of the SELC mode is shown in Fig 3, which includes the base learning models of RF, DT, and gradient boost.…”

Section: Figure 2 Flow Of Lbo D Stacking Ensemble Learning Classifier...mentioning

confidence: 99%

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Rani S,

et al. 2023

ijcsm

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Lung cancer is one of the most serious and prevalent cancers in the globe. Early detection of lung cancer can increase a patient's chances of life. Computed Tomography (CT) scan images are difficult for clinicians to utilize in order to determine the stages of cancer. Computer-aided systems can assist researchers in more precisely predicting the stages of lung cancer in recent times. This study demonstrates the use of technology that is made possible by machine learning and image processing to accurately classify and predict the lung cancer from CT images. The existing tumor detection frameworks have the major difficulties in terms of high complexity, overfitting and error prediction. Therefore, the proposed work aims to formulate a simple and accurate automated system for the prediction and classification of lung cancer from CT images. Before classifying the input lung scan image, an adaptive median filtering approach is used to improve its contrast and quality. From the segmented lung parts, the histogram and texture features are derived. The most relevant characteristics are chosen using the Lion-Butterfly Optimization (LBO) method for training and testing operations. Eventually, the input CT picture is correctly predicted as either healthy or disease-affected using the Stacking Ensemble Learning Classification (SELC) algorithm. In this study, a thorough performance evaluation is conducted utilizing several measures in order to analyze the outcomes

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“…Nowadays, ensemble learning is considered a key technique in the machine learning toolkit and is widely used in industry and the academic world. There are several approaches like bagging [ 18 ], boosting [ 19 ], and staking [ 20 ] for classification tasks in ensemble learning based on the specific application and available data.…”

Section: Introductionmentioning

confidence: 99%

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

Majumder,

Gautam,

Basu

et al. 2024

PLoS ONE

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Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.

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Lung cancer prediction model using ensemble learning techniques and a systematic review analysis

Cited by 39 publications

References 18 publications

Optimizing lung cancer classification through hyperparameter tuning

Optimizing lung cancer classification through hyperparameter tuning

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

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