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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64564
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of computer application
bapuji institute of engineering and technology
Master of computer application
bapuji institute of engineering and technology
Thyroid disorders such as hypothyroidism hyperthyroidism and thyroid cancer are some of the most prevalent endocrine disorders that often go undiagnosed due to mild or asymptomatic symptoms. Early and accurate diagnosis is essential for both prevention and effective treatment. Because they primarily rely on radiological interpretation of ultrasound images and clinical experience conventional diagnostic techniques can result in inconsistent assessments. Applying deep learning and the TI-RADS (Thyroid Imaging Reporting and Data System) standard the proposed system offers an intelligent web-based diagnostic tool that automatically classifies thyroid ultrasound images in deal with this problem. 1. The Flask-built system incorporates a model of a trained Convolutional Neural Network assess uploaded images classify them using TI-RADS categories and provide interpretive insights to support clinical decision- making. The web application provides a robust role-based framework for both administrators and users. User accounts can be managed by administrators add or remove health tips and respond to user questions while users can register log in upload thyroid ultrasound photos for prediction peruse health recommendations and communicate with the admin. By automating the image interpretation process and ensuring readily available health advice the technology promotes early diagnosis lowers diagnostic errors and offers users reliable thyroid health insights. This project offers patients and medical professionals a helpful digital assistant by increasing the efficacy accuracy and accessibility of thyroid illness diagnosis.
KEYWORDS
Deep learning's potential in medical image processing is demonstrated by the automated TI- RADS categorization system. The technology offers rapid, reliable, and educational thyroid diagnosis support by incorporating a CNN-based prediction algorithm within an intuitive interface. Both patients and medical professionals can profit from the system's improved usability thanks to the admin and user modules. The experiment demonstrates how AI can help radiologists and raise patient awareness, which will ultimately lead to more effective thyroid screening.
1. Explainable Automated TI-RADS Evaluation of Thyroid Nodules; Alisa Kunapinun; Dittapong Songsaeng; Sittaya Buathong; Matthew N. Dailey; Chadaporn Keatmanee; Mongkol Ekpanyapong; *Sensors*, 2023, DOI 10.3390/s23167289; ESSN 1424-8220. 2. A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification; Luoyan Wang, Xiaogen Zhou, Xingqing Nie, Xingtao Lin, Jing Li, Haonan Zheng, Ensheng Xue, Shun Chen, Cong Chen, Min Du,Tong Tong, Qinquan Gao, Meijuan Zheng; Frontiers in Neuroscience, 2022; DOI 10.3389/fnins.2022.878 718; ESSN 1662-453X. 3. An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images; Jialin Zhu, Sheng Zhang, Ruiguo Yu, Zhiqiang Liu, Hongyan Gao, Bing Yue, Xun Liu, Xiangqian Zheng, Ming Gao, Xi Wei; Quantitative Imaging in Medicine and Surgery, 2021; DOI 10.21037/qims-20-538; Print ISSN 2223-4292; Online ESSN 2223-4306. 4. TIRADS-Based Artificial Intelligence Systems For Ultrasound Imaging of Thyroid Nodules: A SystematicReview; Yasaman Sharifi, Morteza Danay Ashgzari, Zeinab Naseri, Amin AmiriTehranizadeh 2025; DOI 10.51406/smjmci.v7i10 33; ESSN 2572-9655. 5. Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques; Vijay Vyas Vadhiraj, Andrew Simpkin, James O’Connell, Naykky Singh Ospina, Spyridoula Maraka, Derek T- O’Keeffe; Medicina (Kaunas), 2021; DOI 10.3390/medicina5706 0527; e- ISSN 1648-9144.
Publication Date
6/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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