Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection
Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of...
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Al-Nahrain Journal for Engineering Sciences
2024-12-01
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| Series: | مجلة النهرين للعلوم الهندسية |
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| Online Access: | https://nahje.com/index.php/main/article/view/1217 |
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| author | Ghufran Basim Alghanimi Hadeel Aljobouri Khaleel Akeash Alshimmari Rasha Massoud |
| author_facet | Ghufran Basim Alghanimi Hadeel Aljobouri Khaleel Akeash Alshimmari Rasha Massoud |
| author_sort | Ghufran Basim Alghanimi |
| collection | DOAJ |
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Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.
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| format | Article |
| id | doaj-art-0a776422b0db42188f2f9b0555b98cd9 |
| institution | Kabale University |
| issn | 2521-9154 2521-9162 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Al-Nahrain Journal for Engineering Sciences |
| record_format | Article |
| series | مجلة النهرين للعلوم الهندسية |
| spelling | doaj-art-0a776422b0db42188f2f9b0555b98cd92025-01-11T14:13:19ZengAl-Nahrain Journal for Engineering Sciencesمجلة النهرين للعلوم الهندسية2521-91542521-91622024-12-0127410.29194/NJES.27040396Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound DetectionGhufran Basim Alghanimi0Hadeel Aljobouri1Khaleel Akeash Alshimmari2Rasha Massoud3Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, IraqBiomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad, IraqMinistry of Health, Baghdad Medical City, Baghdad, IraqBiomedical Engineering Department, Faculty of Mechanical and Electrical Engineering, Damascus University., Damascus, Syria Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers. https://nahje.com/index.php/main/article/view/1217Feature SelectionPrincipal Component AnalysisTransfer Learning ResNet50Thyroid NodulesUltrasound |
| spellingShingle | Ghufran Basim Alghanimi Hadeel Aljobouri Khaleel Akeash Alshimmari Rasha Massoud Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection مجلة النهرين للعلوم الهندسية Feature Selection Principal Component Analysis Transfer Learning ResNet50 Thyroid Nodules Ultrasound |
| title | Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection |
| title_full | Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection |
| title_fullStr | Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection |
| title_full_unstemmed | Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection |
| title_short | Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection |
| title_sort | effective feature selection on transfer deep learning algorithm for thyroid nodules ultrasound detection |
| topic | Feature Selection Principal Component Analysis Transfer Learning ResNet50 Thyroid Nodules Ultrasound |
| url | https://nahje.com/index.php/main/article/view/1217 |
| work_keys_str_mv | AT ghufranbasimalghanimi effectivefeatureselectionontransferdeeplearningalgorithmforthyroidnodulesultrasounddetection AT hadeelaljobouri effectivefeatureselectionontransferdeeplearningalgorithmforthyroidnodulesultrasounddetection AT khaleelakeashalshimmari effectivefeatureselectionontransferdeeplearningalgorithmforthyroidnodulesultrasounddetection AT rashamassoud effectivefeatureselectionontransferdeeplearningalgorithmforthyroidnodulesultrasounddetection |