Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach
<p dir="ltr">Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has lim...
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2024
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| _version_ | 1864513509732122624 |
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| author | Zubair Saeed (19325647) |
| author2 | Othmane Bouhali (8252544) Jim Xiuquan Ji (19325650) Rabih Hammoud (14776912) Noora Al-Hammadi (17346808) Souha Aouadi (14776921) Tarraf Torfeh (17760138) |
| author2_role | author author author author author author |
| author_facet | Zubair Saeed (19325647) Othmane Bouhali (8252544) Jim Xiuquan Ji (19325650) Rabih Hammoud (14776912) Noora Al-Hammadi (17346808) Souha Aouadi (14776921) Tarraf Torfeh (17760138) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zubair Saeed (19325647) Othmane Bouhali (8252544) Jim Xiuquan Ji (19325650) Rabih Hammoud (14776912) Noora Al-Hammadi (17346808) Souha Aouadi (14776921) Tarraf Torfeh (17760138) |
| dc.date.none.fl_str_mv | 2024-04-23T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/bioengineering11050410 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Cancerous_and_Non-Cancerous_MRI_Classification_Using_Dual_DCNN_Approach/26490919 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning deep learning magnetic resonance imaging classification machine learning deep convolutional neural networks dual DCNN model inceptionV3 denseNet121 resNet50 resNet34 resNet18 efficientNetB2 squeezeNet VGG16 alexNet leNet-5 learning rates |
| dc.title.none.fl_str_mv | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.</p><h2>Other Information</h2><p dir="ltr">Published in: Bioengineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/bioengineering11050410" target="_blank">https://dx.doi.org/10.3390/bioengineering11050410</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f40fc19a141d50e08bb216931c40d777 |
| identifier_str_mv | 10.3390/bioengineering11050410 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26490919 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN ApproachZubair Saeed (19325647)Othmane Bouhali (8252544)Jim Xiuquan Ji (19325650)Rabih Hammoud (14776912)Noora Al-Hammadi (17346808)Souha Aouadi (14776921)Tarraf Torfeh (17760138)Biomedical and clinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningdeep learningmagnetic resonance imagingclassificationmachine learningdeep convolutional neural networksdual DCNN modelinceptionV3denseNet121resNet50resNet34resNet18efficientNetB2squeezeNetVGG16alexNetleNet-5learning rates<p dir="ltr">Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.</p><h2>Other Information</h2><p dir="ltr">Published in: Bioengineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/bioengineering11050410" target="_blank">https://dx.doi.org/10.3390/bioengineering11050410</a></p>2024-04-23T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/bioengineering11050410https://figshare.com/articles/journal_contribution/Cancerous_and_Non-Cancerous_MRI_Classification_Using_Dual_DCNN_Approach/26490919CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264909192024-04-23T09:00:00Z |
| spellingShingle | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach Zubair Saeed (19325647) Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning deep learning magnetic resonance imaging classification machine learning deep convolutional neural networks dual DCNN model inceptionV3 denseNet121 resNet50 resNet34 resNet18 efficientNetB2 squeezeNet VGG16 alexNet leNet-5 learning rates |
| status_str | publishedVersion |
| title | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| title_full | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| title_fullStr | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| title_full_unstemmed | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| title_short | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| title_sort | Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning deep learning magnetic resonance imaging classification machine learning deep convolutional neural networks dual DCNN model inceptionV3 denseNet121 resNet50 resNet34 resNet18 efficientNetB2 squeezeNet VGG16 alexNet leNet-5 learning rates |