Cascading and Ensemble Techniques in Deep Learning

Zarzà, I. de; Curtò, J. de; Hernández-Orallo, Enrique; Calafate, Carlos T.

doi:10.3390/electronics12153354

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Skin cancer ranks among the most prevalent forms of cancer globally, making up around a third of all reported cancer diagnoses, with its frequency steadily rising each year [ 4 ]. Just in the United States, it is estimated that more than 9500 individuals receive a skin cancer diagnosis daily [ 5 , 6 ].…”

Section: Introductionmentioning

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: Introductionmentioning

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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show abstract

“…Studies have shown that one of the main advantages of ensembles over individual models is their ability to achieve higher-quality indicators of data processing. By aggregating the results of different underlying algorithms, it becomes possible to level out the erroneous predictions obtained from individual models [3]. However, in practice, there are situations where poorly trained classifiers can degrade the results compared to the application of individual algorithms [4].…”

mentioning

confidence: 99%

“…The papers provide various ways of combining ensemble models, but their selection and building are empirical, which does not make it possible to transfer the experience to other subject areas and data structures [2,3]. Another problem with ensemble approaches is that setting optimal parameters requires considerable knowledge and experience and many analysis procedures [4,5].…”

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confidence: 99%

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Lebedev,

Sukhoparov

2024

Emerg Sci J

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This paper presents a solution for building and implementing data processing models and experimentally evaluates new possibilities for improving ensemble methods based on multilevel data processing models. This study proposes a model to reduce the cost of retraining models when transforming data properties. The research objective is to improve the quality indicators of machine learning models when solving classification problems. The novelty is a method that uses a multilevel architecture of data processing models to determine the current data properties in segments at different levels and assign algorithms with the best quality indicators. This method differs from the known ones by using several model levels that analyze data properties and assign the best models to individual segments of data and training. The improvement consists of using unsupervised clustering of data samples. The resulting clusters are separate subsamples for assigning the best machine-learning models and algorithms. Experimental values of quality indicators for different classifiers on the whole sample and different segments were obtained. The findings show that unsupervised clustering using multilevel models can significantly improve the quality indicators of “weak” classifiers. The quality indicators of individual classifiers improve when the number of data clusters is increased to a certain threshold. The results obtained are applicable to classification when developing models and machine learning methods. The proposed method improved the classification quality indicators by 2–9% due to segmentation and the assignment of models with the best quality indicators in individual segments. Doi: 10.28991/ESJ-2024-08-01-025 Full Text: PDF

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Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection

Chengula,

Mwakalonge,

Comert

et al. 2024

Machine Learning with Applications

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Cascading and Ensemble Techniques in Deep Learning

Cited by 6 publications

References 12 publications

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

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Enhancing advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection

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