Ensemble deep learning for brain tumor detection
<p dir="ltr">With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of tra...
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2022
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| author | Shtwai Alsubai (15211962) |
| author2 | Habib Ullah Khan (15862361) Abdullah Alqahtani (7128143) Mohemmed Sha (15862368) Sidra Abbas (8699295) Uzma Ghulam Mohammad4 (15862428) |
| author2_role | author author author author author |
| author_facet | Shtwai Alsubai (15211962) Habib Ullah Khan (15862361) Abdullah Alqahtani (7128143) Mohemmed Sha (15862368) Sidra Abbas (8699295) Uzma Ghulam Mohammad4 (15862428) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shtwai Alsubai (15211962) Habib Ullah Khan (15862361) Abdullah Alqahtani (7128143) Mohemmed Sha (15862368) Sidra Abbas (8699295) Uzma Ghulam Mohammad4 (15862428) |
| dc.date.none.fl_str_mv | 2022-09-02T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fncom.2022.1005617 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Ensemble_deep_learning_for_brain_tumor_detection/23121395 |
| 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 Clinical sciences Neurosciences Oncology and carcinogenesis Information and computing sciences Machine learning brain tumor convolutional neural network long short-term memory CNN-LSTM MR images deep learning |
| dc.title.none.fl_str_mv | Ensemble deep learning for brain tumor detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Computational Neuroscience<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://doi.org/10.3389/fncom.2022.1005617" target="_blank">https://doi.org/10.3389/fncom.2022.1005617</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_78de3bce94c9e8ea94c1b7cb08a4e970 |
| identifier_str_mv | 10.3389/fncom.2022.1005617 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23121395 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ensemble deep learning for brain tumor detectionShtwai Alsubai (15211962)Habib Ullah Khan (15862361)Abdullah Alqahtani (7128143)Mohemmed Sha (15862368)Sidra Abbas (8699295)Uzma Ghulam Mohammad4 (15862428)Biomedical and clinical sciencesClinical sciencesNeurosciencesOncology and carcinogenesisInformation and computing sciencesMachine learningbrain tumorconvolutional neural networklong short-term memoryCNN-LSTMMR imagesdeep learning<p dir="ltr">With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Computational Neuroscience<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://doi.org/10.3389/fncom.2022.1005617" target="_blank">https://doi.org/10.3389/fncom.2022.1005617</a></p>2022-09-02T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fncom.2022.1005617https://figshare.com/articles/journal_contribution/Ensemble_deep_learning_for_brain_tumor_detection/23121395CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/231213952022-09-02T06:00:00Z |
| spellingShingle | Ensemble deep learning for brain tumor detection Shtwai Alsubai (15211962) Biomedical and clinical sciences Clinical sciences Neurosciences Oncology and carcinogenesis Information and computing sciences Machine learning brain tumor convolutional neural network long short-term memory CNN-LSTM MR images deep learning |
| status_str | publishedVersion |
| title | Ensemble deep learning for brain tumor detection |
| title_full | Ensemble deep learning for brain tumor detection |
| title_fullStr | Ensemble deep learning for brain tumor detection |
| title_full_unstemmed | Ensemble deep learning for brain tumor detection |
| title_short | Ensemble deep learning for brain tumor detection |
| title_sort | Ensemble deep learning for brain tumor detection |
| topic | Biomedical and clinical sciences Clinical sciences Neurosciences Oncology and carcinogenesis Information and computing sciences Machine learning brain tumor convolutional neural network long short-term memory CNN-LSTM MR images deep learning |