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@article{195500,
        author = {M Charan Kumar Reddy and M Rithwik and P Deepak Chowdary and G Vinay Yadav},
        title = {Data-Driven Analysis and Prediction of Thyroid Trends in Women},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {321-323},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195500},
        abstract = {Endocrine health challenges, specifically those involving the thyroid gland, represent a major global medical concern that predominantly impacts the female population. While clinical practitioners rely heavily on ultrasound imaging for diagnosis, the process is often hampered by subjective interpretation and the varying experience levels of medical staff. To mitigate these inconsistencies, we engineered an automated diagnostic framework that utilises advanced neural networks to categorise thyroid scans into two distinct groups: healthy and pathological.
Our methodology employs a streamlined, pre-trained image recognition model known for its high-performance benchmarks and minimal computational footprint. We trained this system on a comprehensive library of 5,000 verified ultrasound samples. By integrating rigorous pre-processing techniques—such as image normalisation and artificial data expansion—we ensured the model remains robust across various clinical scenarios. Finally, we deployed the tool as a web-accessible application, providing medical professionals with a responsive and reliable secondary opinion to assist in early disease identification.},
        keywords = {},
        month = {April},
        }