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Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction

Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the...

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Autores principales: Pintelas, Emmanuel, Liaskos, Meletis, Livieris, Ioannis E., Kotsiantis, Sotiris, Pintelas, Panagiotis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321040/
https://www.ncbi.nlm.nih.gov/pubmed/34460583
http://dx.doi.org/10.3390/jimaging6060037
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author Pintelas, Emmanuel
Liaskos, Meletis
Livieris, Ioannis E.
Kotsiantis, Sotiris
Pintelas, Panagiotis
author_facet Pintelas, Emmanuel
Liaskos, Meletis
Livieris, Ioannis E.
Kotsiantis, Sotiris
Pintelas, Panagiotis
author_sort Pintelas, Emmanuel
collection PubMed
description Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.
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spelling pubmed-83210402021-08-26 Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction Pintelas, Emmanuel Liaskos, Meletis Livieris, Ioannis E. Kotsiantis, Sotiris Pintelas, Panagiotis J Imaging Article Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms. MDPI 2020-05-28 /pmc/articles/PMC8321040/ /pubmed/34460583 http://dx.doi.org/10.3390/jimaging6060037 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Pintelas, Emmanuel
Liaskos, Meletis
Livieris, Ioannis E.
Kotsiantis, Sotiris
Pintelas, Panagiotis
Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
title Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
title_full Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
title_fullStr Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
title_full_unstemmed Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
title_short Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction
title_sort explainable machine learning framework for image classification problems: case study on glioma cancer prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321040/
https://www.ncbi.nlm.nih.gov/pubmed/34460583
http://dx.doi.org/10.3390/jimaging6060037
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