Cargando…
MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique
Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medic...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424498/ https://www.ncbi.nlm.nih.gov/pubmed/36052321 http://dx.doi.org/10.3389/fmed.2022.924979 |
_version_ | 1784778234849656832 |
---|---|
author | Montaha, Sidratul Azam, Sami Rafid, A. K. M. Rakibul Haque Hasan, Md. Zahid Karim, Asif Hasib, Khan Md. Patel, Shobhit K. Jonkman, Mirjam Mannan, Zubaer Ibna |
author_facet | Montaha, Sidratul Azam, Sami Rafid, A. K. M. Rakibul Haque Hasan, Md. Zahid Karim, Asif Hasib, Khan Md. Patel, Shobhit K. Jonkman, Mirjam Mannan, Zubaer Ibna |
author_sort | Montaha, Sidratul |
collection | PubMed |
description | Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques. |
format | Online Article Text |
id | pubmed-9424498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94244982022-08-31 MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique Montaha, Sidratul Azam, Sami Rafid, A. K. M. Rakibul Haque Hasan, Md. Zahid Karim, Asif Hasib, Khan Md. Patel, Shobhit K. Jonkman, Mirjam Mannan, Zubaer Ibna Front Med (Lausanne) Medicine Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques. Frontiers Media S.A. 2022-08-16 /pmc/articles/PMC9424498/ /pubmed/36052321 http://dx.doi.org/10.3389/fmed.2022.924979 Text en Copyright © 2022 Montaha, Azam, Rafid, Hasan, Karim, Hasib, Patel, Jonkman and Mannan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Montaha, Sidratul Azam, Sami Rafid, A. K. M. Rakibul Haque Hasan, Md. Zahid Karim, Asif Hasib, Khan Md. Patel, Shobhit K. Jonkman, Mirjam Mannan, Zubaer Ibna MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
title | MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
title_full | MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
title_fullStr | MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
title_full_unstemmed | MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
title_short | MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
title_sort | mnet-10: a robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424498/ https://www.ncbi.nlm.nih.gov/pubmed/36052321 http://dx.doi.org/10.3389/fmed.2022.924979 |
work_keys_str_mv | AT montahasidratul mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT azamsami mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT rafidakmrakibulhaque mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT hasanmdzahid mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT karimasif mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT hasibkhanmd mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT patelshobhitk mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT jonkmanmirjam mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique AT mannanzubaeribna mnet10arobustshallowconvolutionalneuralnetworkmodelperformingablationstudyonmedicalimagesassessingtheeffectivenessofapplyingoptimaldataaugmentationtechnique |