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Classification of lung cancer from histopathology Images using a deep ensemble classifier

Lung cancer continues to be the leading disease of patient death and disability all over the world. Many metabolic abnormalities and genetic illnesses, including cancer, can be fatal. Histological diagnosis one of the important part to determine form of malignancy. Thus, one of the most significant...

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Main Authors: Singh, Onkar, Singh, Koushlendra Kumar, Das, Saikat, Akbari, Akbar Sheikh, Abd Manap, Nurulfajar
Format: Conference or Workshop Item
Language:English
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27997/1/Classification%20of%20lung%20cancer%20from%20histopathology%20Images%20using%20a%20deep%20ensemble%20classifier.pdf
http://eprints.utem.edu.my/id/eprint/27997/
https://eprints.leedsbeckett.ac.uk/id/eprint/10472/
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spelling my.utem.eprints.279972024-10-16T16:41:27Z http://eprints.utem.edu.my/id/eprint/27997/ Classification of lung cancer from histopathology Images using a deep ensemble classifier Singh, Onkar Singh, Koushlendra Kumar Das, Saikat Akbari, Akbar Sheikh Abd Manap, Nurulfajar Lung cancer continues to be the leading disease of patient death and disability all over the world. Many metabolic abnormalities and genetic illnesses, including cancer, can be fatal. Histological diagnosis one of the important part to determine form of malignancy. Thus, one of the most significant research challenges is explore the classification of lung cancer based on histopathology images. The proposed method encompasses the ensemble learning for classification of lung cancer and its subtype which employing pre-train deep learning models (EfficientNetB3, InceptionNetV2, ResNet50, and VGG16). The ensemble model has been created utilizing VotingClassifier in soft voting mode. The ensemble model is fit using the extracted features (features_train) and training labels (y_train). The LC25000 database's images of lung tissues are utilized to train and evaluate the ensemble classifiers. Our proposed method has an average F_I score of 99.33%, recall of 99.33%, precision of 99.33%, and accuracy of 99.00% for lung cancer detection. The findings of the analysis demonstrate that our proposed approach performs noticeably better compared to existing models. This technology is more suited to handle a wide range of classification challenges than using a single classifier alone and could improve the accuracy of predictions. 2023 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27997/1/Classification%20of%20lung%20cancer%20from%20histopathology%20Images%20using%20a%20deep%20ensemble%20classifier.pdf Singh, Onkar and Singh, Koushlendra Kumar and Das, Saikat and Akbari, Akbar Sheikh and Abd Manap, Nurulfajar (2023) Classification of lung cancer from histopathology Images using a deep ensemble classifier. In: 2023 IEEE International Conference on Imaging Systems and Techniques, IST 2023, 17 October 2023through 19 October 2023, Copenhagen. https://eprints.leedsbeckett.ac.uk/id/eprint/10472/
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Lung cancer continues to be the leading disease of patient death and disability all over the world. Many metabolic abnormalities and genetic illnesses, including cancer, can be fatal. Histological diagnosis one of the important part to determine form of malignancy. Thus, one of the most significant research challenges is explore the classification of lung cancer based on histopathology images. The proposed method encompasses the ensemble learning for classification of lung cancer and its subtype which employing pre-train deep learning models (EfficientNetB3, InceptionNetV2, ResNet50, and VGG16). The ensemble model has been created utilizing VotingClassifier in soft voting mode. The ensemble model is fit using the extracted features (features_train) and training labels (y_train). The LC25000 database's images of lung tissues are utilized to train and evaluate the ensemble classifiers. Our proposed method has an average F_I score of 99.33%, recall of 99.33%, precision of 99.33%, and accuracy of 99.00% for lung cancer detection. The findings of the analysis demonstrate that our proposed approach performs noticeably better compared to existing models. This technology is more suited to handle a wide range of classification challenges than using a single classifier alone and could improve the accuracy of predictions.
format Conference or Workshop Item
author Singh, Onkar
Singh, Koushlendra Kumar
Das, Saikat
Akbari, Akbar Sheikh
Abd Manap, Nurulfajar
spellingShingle Singh, Onkar
Singh, Koushlendra Kumar
Das, Saikat
Akbari, Akbar Sheikh
Abd Manap, Nurulfajar
Classification of lung cancer from histopathology Images using a deep ensemble classifier
author_facet Singh, Onkar
Singh, Koushlendra Kumar
Das, Saikat
Akbari, Akbar Sheikh
Abd Manap, Nurulfajar
author_sort Singh, Onkar
title Classification of lung cancer from histopathology Images using a deep ensemble classifier
title_short Classification of lung cancer from histopathology Images using a deep ensemble classifier
title_full Classification of lung cancer from histopathology Images using a deep ensemble classifier
title_fullStr Classification of lung cancer from histopathology Images using a deep ensemble classifier
title_full_unstemmed Classification of lung cancer from histopathology Images using a deep ensemble classifier
title_sort classification of lung cancer from histopathology images using a deep ensemble classifier
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27997/1/Classification%20of%20lung%20cancer%20from%20histopathology%20Images%20using%20a%20deep%20ensemble%20classifier.pdf
http://eprints.utem.edu.my/id/eprint/27997/
https://eprints.leedsbeckett.ac.uk/id/eprint/10472/
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score 13.211869

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