Neural harmony: revolutionizing thyroid nodule diagnosis with hybrid networks and genetic algorithms

References

• Abbad Ur Rehman H, Lin C-Y, Mushtaq Z. 2021. Effective K-nearest neighbor algorithms performance analysis of thyroid disease. J Chin Inst Eng. 44(1):77–87. doi: 10.1080/02533839.2020.1831967.
   Web of Science ®Google Scholar
• Afshan N, Mushtaq Z, Alamri FS, Qureshi MF, Khan NA, Siddique I. 2023. Efficient thyroid disorder identification with weighted voting ensemble of super learners by using adaptive synthetic sampling technique. MATH. 8(10):24274–24309. doi: 10.3934/math.20231238.
   Google Scholar
• Akhtar T, Gilani SO, Mushtaq Z, Arif S, Jamil M, Ayaz Y, Butt SI, Waris A. 2021. Effective voting ensemble of homogenous ensembling with multiple attribute-selection approaches for improved identification of thyroid disorder. Electronics. 10(23):3026. doi: 10.3390/electronics10233026.
   Web of Science ®Google Scholar
• Alshayeji MH. 2023. Early thyroid risk prediction by data mining and ensemble classifiers. MAKE. 5(3):1195–1213. doi: 10.3390/make5030061.
   Google Scholar
• Alyas T, Hamid M, Alissa K, Faiz T, Tabassum N, Ahmad A. 2022. Empirical method for thyroid disease classification using a machine learning approach. Biomed Res Int. 2022:9809932–9809910. doi: 10.1155/2022/9809932.
   PubMed Web of Science ®Google Scholar
• Arjaria SK. 2022. Developing an explainable machine learning-based thyroid disease prediction model. IJBAN. 9:1–18. doi: 10.4018/IJBAN.292058.
   Google Scholar
• Aversano L, Bernardi M, Cimitile M, Iammarino M, Macchia PE, Nettore IC, Verdone, C. 2021. Thyroid disease treatment prediction with machine learning approaches. J PROCS. 192:1031–1040. doi: 10.1016/j.procs.2021.08.106.
   Google Scholar
• Aversano L, Bernardi ML, Cimitile M, Maiellaro A, Pecori R. 2023. A systematic review on artificial intelligence techniques for detecting thyroid diseases. PeerJ Comput Sci. 9:e1394. doi: 10.7717/peerj-cs.1394.
   PubMed Web of Science ®Google Scholar
• Bhatia T. 2023. Deep learning-based approach for thyroid dysfunction prediction. IRJET. 10: 2395
   Google Scholar
• Chaubey G, Bisen D, Arjaria S, Yadav V. 2021. Thyroid disease prediction using machine learning approaches. Natl Acad Sci Lett. 44(3):233–238. doi: 10.1007/s40009-020-00979-z.
   Web of Science ®Google Scholar
• DDTI: Thyroid Ultrasound Images. 2022. DDTI: Thyroid ultrasound images 2022. [Accessed 2023 December 21] https://www.kaggle.com/datasets/dasmehdixtr/ddti-thyroid-ultrasound-images
   Google Scholar
• Deng P, Han X, Wei X, Chang L. 2022. Automatic classification of thyroid nodules in ultrasound images using a multi-task attention network guided by clinical knowledge. Comput Biol Med. 150:106172. doi: 10.1016/j.compbiomed.2022.106172.
   PubMed Web of Science ®Google Scholar
• Dhanasekaran S, Mathiyalagan P, Rajeshwaran AM. 2021. Automatic segmentation of lung tumors using adaptive neuron-fuzzy inference system. Ann Rom Soc Cell Biol. 25:17468–17483.
   Google Scholar
• Gong H, Cheng H, Xie Y, Tan S, Chen G, Chen F, Li G. 2022. Less is more: adaptive curriculum learning for thyroid nodule diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV, 248–257. doi: 10.1007/978-3-031-16440-8_24.
   Google Scholar
• He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 770–778.
   Google Scholar
• Hossain MB, Shama A, Adhikary A, Raha AD, Uddin KMA, Hossain MA, Islam I, Murad SA, Munir MS, Bairagi AK, et al. 2023. An explainable artificial intelligence framework for the predictive analysis of hypo and hyper thyroidism using machine learning algorithms. Hum-Cent Intell Syst. 3(3):211–231. doi: 10.1007/s44230-023-00027-1.
   Google Scholar
• Islam SS, Haque MS, Miah MSU, Sarwar TB, Nugraha R. 2022. Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study. PeerJ Comput Sci. 8:e898. doi: 10.7717/peerj-cs.898.
   PubMed Web of Science ®Google Scholar
• Juneja K. 2022. Expanded and filtered features based elm model for thyroid disease classification. Wireless Pers Commun. 126(2):1805–1842. doi: 10.1007/s11277-022-09823-7.
   Web of Science ®Google Scholar
• Karthikeyan S, Ramkumar G, Aravindkumar S, Tamilselvi M, Ramesh S, Ranjith A. 2022. A novel deep learning-based black fungus disease identification using modified hybrid learning methodology. Contrast Media Mol Imaging. 2022:4352730–4352711. doi: 10.1155/2022/4352730.
   PubMed Web of Science ®Google Scholar
• Mandala V, Senthilnathan T, Suganyadevi S, Gobhinat S, Selvaraj D, Dhanapal R. 2023. An optimized back propagation neural network for automated evaluation of health condition using sensor data. Measure Sens. 29:100846. doi: 10.1016/j.measen.2023.100846.
   Google Scholar
• Naeem AB, Senapati B, Chauhan AS, Makhija M, Singh A, Gupta M, Tiwari PK, Abdel-Rehim WMF. 2023. Hypothyroidism disease diagnosis by using machine learning algorithms. IJISAE. 11(3):368–373. https://ijisae.org/index.php/IJISAE/article/view/3178.
   Google Scholar
• Pal M, Parija S, Panda G. 2022. Enhanced prediction of thyroid disease using machine learning method. VLSI DCS, India. doi: 10.1109/VLSIDCS53788.2022.9811472.
   Google Scholar
• Prathibha S, Dahiya D, R. Rene Robin C, Venkata Nishkala C, Swedha S. 2023. A novel technique for detecting various thyroid diseases using deep learning. Intelligent Automat Soft Comput. 35(1):199–214. doi: 10.32604/iasc.2023.025819.
   Web of Science ®Google Scholar
• Rufus NHA, Anand D, Rama RS, Kumar A, Vigneshwar AS. 2022. Evolutionary optimization with deep transfer learning for content based image retrieval in cloud environment. ICAISS.:826–831.
   Google Scholar
• Shiuh TL, Khai WK, Xin YC, Wai CY. 2023. Prediction of thyroid disease using machine learning approaches and featurewiz selection. JTEC. https://jtec.utem.edu.my/jtec/article/view/6274.
   Google Scholar
• Sivasaravanan S, Jayalakshmi V. 2021. Thyroid disease classification in big data on machine learning. IJSRSET. 9(1): 30–37.
   Google Scholar
• Sultana A, Islam R. 2023. Machine learning framework with feature selection approaches for thyroid disease classification and associated risk factors identification. J Electr Syst Inf Technol. 10(1):1–23. doi: 10.1186/s43067-023-00101-5.
   Google Scholar
• Thabit QQ, Ibrahim A, Fahad TO. 2023. Thyroid disease prediction with machine learning algorithms. Research Gate. 18:229–237.
   Google Scholar
• Viswanatha V, Ramachandra AC, Sankar RR, Mahulikar CRS. 2023. Thyroid disease detection using machine learning approach. J Xi’an Univ Architect Technol. 15:327–333.
   Google Scholar
• Zhang X, Lee VC, Rong J, Lee JC, Liu F. 2022. Deep convolutional neural networks in thyroid disease detection: a multi-classification comparison by ultrasonography and computed tomography. Comput Methods Programs Biomed. 220:106823. ISSN 0169-2607. doi: 10.1016/j.cmpb.2022.106823.
   PubMed Web of Science ®Google Scholar
• Zhao H, Zheng C, Zhang H, Rao M, Li Y, Fang D, Huang J, Zhang W, Yuan G. 2023. Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study. FENDO Sec. Thyroid Endocrinol. 14:1–12. doi: 10.3389/fendo.2023.1224191.
   Web of Science ®Google Scholar
• Zhao S-X, Chen Y, Yang K-F, Luo Y, Ma B-Y, Li Y-J. 2022. A local and global feature disentangled network: toward classification of benign-malignant thyroid nodules from ultrasound image. IEEE Trans Med Imaging. 41(6):1497–1509. doi: 10.1109/TMI.2022.3140797.
   PubMed Web of Science ®Google Scholar