Conclusions

Akanbi, Oluwatobi Ayodeji; Amiri, Iraj Sadegh; Fazeldehkordi, Elahe

doi:10.1016/b978-0-12-802927-5.00006-x

Cited by 2 publications

(2 citation statements)

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“…To augment classification outcomes, this approach employs a majority-based voting mechanism. For each test instance, the classification results are independently computed by each of the specified models, and the ultimate output is predicted based on the outcomes that achieve majority representation [ 87 , 88 , 89 , 90 ]. In the context of majority voting, the class label y is forecasted by virtue of a majority (plurality) consensus reached by the individual classifiers, denoted as C.

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Section: Proposed Methodologymentioning

confidence: 99%

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Hossain,

Arefin

et al. 2023

Diagnostics

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Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin cancer classification, ensemble methods offer a pathway for further enhancing diagnostic accuracy. This study introduces a cutting-edge approach employing the Max Voting Ensemble Technique for robust skin cancer classification on ISIC 2018: Task 1-2 dataset. We incorporate a range of cutting-edge, pre-trained deep neural networks, including MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, and Xception. These models have been extensively trained on skin cancer datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages the synergistic capabilities of these models by combining their complementary features to elevate classification performance further. In our approach, input images undergo preprocessing for model compatibility. The ensemble integrates the pre-trained models with their architectures and weights preserved. For each skin lesion image under examination, every model produces a prediction. These are subsequently aggregated using the max voting ensemble technique to yield the final classification, with the majority-voted class serving as the conclusive prediction. Through comprehensive testing on a diverse dataset, our ensemble outperformed individual models, attaining an accuracy of 93.18% and an AUC score of 0.9320, thus demonstrating superior diagnostic reliability and accuracy. We evaluated the effectiveness of our proposed method on the HAM10000 dataset to ensure its generalizability. Our ensemble method delivers a robust, reliable, and effective tool for the classification of skin cancer. By utilizing the power of advanced deep neural networks, we aim to assist healthcare professionals in achieving timely and accurate diagnoses, ultimately reducing mortality rates and enhancing patient outcomes.

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…”

Section: Proposed Methodologymentioning

confidence: 99%

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Hossain,

Arefin

et al. 2023

Diagnostics

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“…The errors of the ventilation indices in evaluating the airborne infection risk control performance are quantitively evaluated based on a normalization analysis. Because the scales and units for CDI , AUE , CRE , ACE , AoA, and airborne infection risk are different, the ventilation indices and airborne infection risk are rescaled to be between 0 and 1 by the min-max normalization [ [33] , [34] , [35] ], so they can be equally compared. The min-max normalization has been widely used to normalize parameters of different scales and units for fair and reasonable comparisons [ [33] , [34] , [35] ].…”

Section: Methodsmentioning

confidence: 99%

Ventilation indices for evaluation of airborne infection risk control performance of air distribution

Niu

Zhang

et al. 2022

Building and Environment

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Conclusions

Cited by 2 publications

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Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Ventilation indices for evaluation of airborne infection risk control performance of air distribution

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