RETRACTED ARTICLE: Advanced lung cancer prediction based on blockchain material using extended CNN

Srinivasulu, Asadi; Ramanjaneyulu, K.; Neelaveni, R.; Karanam, Santoshachandra Rao; Majji, Sankararao; Jothilingam, Muthukumaran; Patnala, Tulasi Radhika

doi:10.1007/s13204-021-01897-2

Cited by 31 publications

(6 citation statements)

References 26 publications

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“…Artificial intelligence (AI) and deep learning for determining food classification, quality, and nutrition have shown their implications in the literature. eir application would increase food industry automation, increase food safety, and generate higher income through tourism [7,8]. us, researchers in this research study are going to investigate the key role played by blockchain materials in detecting lung cancer and monitoring food quality easier than before by applying the CNN method.…”

Section: Introductionmentioning

confidence: 99%

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Aboamer

Sikkandar

Gupta

et al. 2022

Journal of Food Quality

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Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding.

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Section: Introductionmentioning

confidence: 99%

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Aboamer

Sikkandar

Gupta

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…ML and DL models are extensively being used in medical diagnostics, including breast cancer diagnosis [ 22 ], lung cancer detection [ 23 ], colorectal cancer [ 24 ], and numerous other healthcare applications [ 25 – 27 ]. Some research works applied DL approaches in various tasks like ALzheimer’s diagnosis [ 28 ], medical imaging [ 29 ], and pathology image segmentation [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Munshi

2024

PLoS ONE

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Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models’ efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model’s capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study’s findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention.

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“…In comparison to the trained radiologists, the latest cyber-physical technologies and computer-aided detection with Deep Learning has shown promising results in lung cancer diagnosis [127,126,32]. Many different types of Deep Learning [102] architectures have been studied to understand better how to diagnose lung illness. In [102], a 3D multipath VGG-like system with two setups is suggested.…”

Section: Diagnosis Of Lungmentioning

confidence: 99%

“…Many different types of Deep Learning [102] architectures have been studied to understand better how to diagnose lung illness. In [102], a 3D multipath VGG-like system with two setups is suggested.…”

Section: Diagnosis Of Lungmentioning

confidence: 99%

Convolution Neural Networks for Disease Prediction: Applications and Challenges

Mushtaq,

Singh

2024

SCPE

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More people are using Deep Learning techniques in the healthcare field as a result of the quick development in domains like Computer Vision, Graphics Processing Technology, and the accessibility of medical imaging datasets. Convolutional Neural Networks (CNNs), in particular, have quickly emerged as the preferred technique for processing clinical data. CNN-based designs have been embraced by the diagnostic imaging group to assist physicians with disease identification. Since AlexNet's enormous success in 2012, CNNs have indeed been employed more and more in the analysis of medical images to boost the effectiveness of physicians. This article summarises various CNN architectures for predicting medical diseases and their challenges. We examine the utilization of Deep Learning for the prediction of various diseases, including Brain diseases, Diabetic Retinopathy, and Lung cancer. This research also provides a survey of datasets available for analysis.

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RETRACTED ARTICLE: Advanced lung cancer prediction based on blockchain material using extended CNN

Cited by 31 publications

References 26 publications

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Convolution Neural Networks for Disease Prediction: Applications and Challenges

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