A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance
<p dir="ltr">A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513541452595200 |
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| author | Krishnamoorthy Natarajan (22047464) |
| author2 | Suresh Muthusamy (11866462) Mizaj Shabil Sha (17714286) Kishor Kumar Sadasivuni (8036039) Sreejith Sekaran (22047467) Christober Asir Rajan Charles Gnanakkan (22047470) Ahmed A.Elngar (22047473) |
| author2_role | author author author author author author |
| author_facet | Krishnamoorthy Natarajan (22047464) Suresh Muthusamy (11866462) Mizaj Shabil Sha (17714286) Kishor Kumar Sadasivuni (8036039) Sreejith Sekaran (22047467) Christober Asir Rajan Charles Gnanakkan (22047470) Ahmed A.Elngar (22047473) |
| author_role | author |
| dc.creator.none.fl_str_mv | Krishnamoorthy Natarajan (22047464) Suresh Muthusamy (11866462) Mizaj Shabil Sha (17714286) Kishor Kumar Sadasivuni (8036039) Sreejith Sekaran (22047467) Christober Asir Rajan Charles Gnanakkan (22047470) Ahmed A.Elngar (22047473) |
| dc.date.none.fl_str_mv | 2024-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-024-09900-x |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_method_for_the_detection_and_classification_of_multiple_diseases_using_transfer_learning-based_deep_learning_techniques_with_improved_performance/29900414 |
| 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 Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Eye diseases Skin diseases Brain tumors CT scans MRI Chest X-rays Retinal fundus ımages VGG16 VGG19 ResNet InceptionV3 Learning rate annealing EfficientNetB4 |
| dc.title.none.fl_str_mv | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc.,</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<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/10.1007/s00521-024-09900-x" target="_blank">https://dx.doi.org/10.1007/s00521-024-09900-x</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2759613e6607e4f42ccb13d3dc8cafe0 |
| identifier_str_mv | 10.1007/s00521-024-09900-x |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29900414 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performanceKrishnamoorthy Natarajan (22047464)Suresh Muthusamy (11866462)Mizaj Shabil Sha (17714286)Kishor Kumar Sadasivuni (8036039)Sreejith Sekaran (22047467)Christober Asir Rajan Charles Gnanakkan (22047470)Ahmed A.Elngar (22047473)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningEye diseasesSkin diseasesBrain tumorsCT scansMRIChest X-raysRetinal fundus ımagesVGG16VGG19ResNetInceptionV3Learning rate annealingEfficientNetB4<p dir="ltr">A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc.,</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<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/10.1007/s00521-024-09900-x" target="_blank">https://dx.doi.org/10.1007/s00521-024-09900-x</a></p>2024-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-024-09900-xhttps://figshare.com/articles/journal_contribution/A_novel_method_for_the_detection_and_classification_of_multiple_diseases_using_transfer_learning-based_deep_learning_techniques_with_improved_performance/29900414CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299004142024-08-01T00:00:00Z |
| spellingShingle | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance Krishnamoorthy Natarajan (22047464) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Eye diseases Skin diseases Brain tumors CT scans MRI Chest X-rays Retinal fundus ımages VGG16 VGG19 ResNet InceptionV3 Learning rate annealing EfficientNetB4 |
| status_str | publishedVersion |
| title | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| title_full | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| title_fullStr | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| title_full_unstemmed | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| title_short | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| title_sort | A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Eye diseases Skin diseases Brain tumors CT scans MRI Chest X-rays Retinal fundus ımages VGG16 VGG19 ResNet InceptionV3 Learning rate annealing EfficientNetB4 |