Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique
In the field of medical research, prediction, as well as diagnosis of thyroid disease, is a major cause that is a challenging onset axiom. In metabolism regulation, thyroid hormone secretions play a significant role. Two frequent thyroid diseases are hypothyroidism and hyperthyroidism that release t...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10163762/ |
| _version_ | 1797782837417476096 |
|---|---|
| author | E. Mohan P. Saravanan Balaji Natarajan S. V. Aswin Kumer G. Sambasivam G. Prabu Kanna Vaibhav Bhushan Tyagi |
| author_facet | E. Mohan P. Saravanan Balaji Natarajan S. V. Aswin Kumer G. Sambasivam G. Prabu Kanna Vaibhav Bhushan Tyagi |
| author_sort | E. Mohan |
| collection | DOAJ |
| description | In the field of medical research, prediction, as well as diagnosis of thyroid disease, is a major cause that is a challenging onset axiom. In metabolism regulation, thyroid hormone secretions play a significant role. Two frequent thyroid diseases are hypothyroidism and hyperthyroidism that release the hormones like the thyroid, which regulate the body’s metabolism rate. For analytics, the approach of data cleansing is utilized to analyze enough primitive data, which demonstrates the patients’ risk. Deep Neural Networks (DNN) is the most vital as well as efficient technology, which predict the disorder of thyroid. To avoid the errors of human, the evaluation of manual process consumes expertise domain as well as time. To detect disease, a novel Long Short-Term Memory based Convolution Neural Network (LSTM-CNN) is utilized with occurrence area Vgg-19. For selecting the feature, the approach of bias field correction is integrated with the hybrid optimization technique i.e., Black Widow Optimization as well as Mayfly Optimization Approach (HBWO-MOA), also for classifying the disease the LSTM as well as Vgg-19 of Deep Learning (DL) is presented. From DDTI dataset image of ultrasound, the disease of thyroid prediction as well as classification is efficiency. This analysis shown that the proposed technology is accurate than the convolutional methodology. When compared to existing prediction techniques i.e., AlexNet-LSTM, ResNet-LSTM, Vgg16-LSTM, the proposed approach of Vgg-19-LSTM’s precision, sensitivity, accuracy, recalls as well as F1_score is effective. |
| first_indexed | 2024-03-13T00:17:35Z |
| format | Article |
| id | doaj.art-a3642218ffca487aa25a2d2af5cd73cc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T00:17:35Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a3642218ffca487aa25a2d2af5cd73cc2023-07-11T23:00:25ZengIEEEIEEE Access2169-35362023-01-0111681276813810.1109/ACCESS.2023.328951110163762Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM TechniqueE. Mohan0P. Saravanan1Balaji Natarajan2https://orcid.org/0000-0003-0040-9271S. V. Aswin Kumer3https://orcid.org/0000-0002-0511-3085G. Sambasivam4https://orcid.org/0000-0002-7407-4796G. Prabu Kanna5Vaibhav Bhushan Tyagi6https://orcid.org/0000-0001-8153-3607Department of ECE, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, IndiaDepartment of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Sri Venkateshwaraa College of Engineering and Technology, Ariyur, Puducherry, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, IndiaSchool of Computing and Data Science, Xiamen University Malaysia, Sepang, Selangor, MalaysiaSchool of Computing Science and Engineering, VIT Bhopal University, Kothri Kalan, Sehore, Madhya Pradesh, IndiaFaculty of Engineering, ISBAT University, Kampala, UgandaIn the field of medical research, prediction, as well as diagnosis of thyroid disease, is a major cause that is a challenging onset axiom. In metabolism regulation, thyroid hormone secretions play a significant role. Two frequent thyroid diseases are hypothyroidism and hyperthyroidism that release the hormones like the thyroid, which regulate the body’s metabolism rate. For analytics, the approach of data cleansing is utilized to analyze enough primitive data, which demonstrates the patients’ risk. Deep Neural Networks (DNN) is the most vital as well as efficient technology, which predict the disorder of thyroid. To avoid the errors of human, the evaluation of manual process consumes expertise domain as well as time. To detect disease, a novel Long Short-Term Memory based Convolution Neural Network (LSTM-CNN) is utilized with occurrence area Vgg-19. For selecting the feature, the approach of bias field correction is integrated with the hybrid optimization technique i.e., Black Widow Optimization as well as Mayfly Optimization Approach (HBWO-MOA), also for classifying the disease the LSTM as well as Vgg-19 of Deep Learning (DL) is presented. From DDTI dataset image of ultrasound, the disease of thyroid prediction as well as classification is efficiency. This analysis shown that the proposed technology is accurate than the convolutional methodology. When compared to existing prediction techniques i.e., AlexNet-LSTM, ResNet-LSTM, Vgg16-LSTM, the proposed approach of Vgg-19-LSTM’s precision, sensitivity, accuracy, recalls as well as F1_score is effective.https://ieeexplore.ieee.org/document/10163762/ClassificationHMOA-BWOLSTMpre-processingsegmentationVgg-19 |
| spellingShingle | E. Mohan P. Saravanan Balaji Natarajan S. V. Aswin Kumer G. Sambasivam G. Prabu Kanna Vaibhav Bhushan Tyagi Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique IEEE Access Classification HMOA-BWO LSTM pre-processing segmentation Vgg-19 |
| title | Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique |
| title_full | Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique |
| title_fullStr | Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique |
| title_full_unstemmed | Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique |
| title_short | Thyroid Detection and Classification Using DNN Based on Hybrid Meta-Heuristic and LSTM Technique |
| title_sort | thyroid detection and classification using dnn based on hybrid meta heuristic and lstm technique |
| topic | Classification HMOA-BWO LSTM pre-processing segmentation Vgg-19 |
| url | https://ieeexplore.ieee.org/document/10163762/ |
| work_keys_str_mv | AT emohan thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique AT psaravanan thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique AT balajinatarajan thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique AT svaswinkumer thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique AT gsambasivam thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique AT gprabukanna thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique AT vaibhavbhushantyagi thyroiddetectionandclassificationusingdnnbasedonhybridmetaheuristicandlstmtechnique |