<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.</p>
</abstract>

In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Department of Electronics & Telecommunication

Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale

Artificial Intelligence and Machine Learning Department

Symbiosis Institute of Technology, Symbiosis International (Deemed) University

Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney

Universiti Kebangsaan, Institute of Climate Change

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have...

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Article Category: Article

Publié en ligne: 06 mars 2024

Pages: -

Reçu: 07 sept. 2023

DOI: https://doi.org/10.2478/ijssis-2024-0013

Mots clés
leukemia, classification, ensemble, VGG-16, Inception, SHAP, LIME, GradCAM

© 2024 Nilkanth Mukund Deshpande et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Article Category: Article

Publié en ligne: 06 mars 2024

Pages: -

Reçu: 07 sept. 2023

DOI: https://doi.org/10.2478/ijssis-2024-0013

Mots clésleukemia, classification, ensemble, VGG-16, Inception, SHAP, LIME, GradCAM

© 2024 Nilkanth Mukund Deshpande et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
leukemia, classification, ensemble, VGG-16, Inception, SHAP, LIME, GradCAM