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Employing two well-known deep learning models, ResNet50 and VGG16, in a novel way to identify thyroid lesions. The identification of thyroid nodules, which are atypical growths that arise inside the thyroid gland, is of paramount importance in the diagnosis of thyroid conditions and possible cancers.

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IshraqueNiloy07/Research---Automated-Detection-System-of-Thyroid-Cancer

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Research - Automated Detection System of Thyroid Cancer

This repository offers a novel method for detecting thyroid nodules using the widely used deep learning models VGG16 and ResNet50. Unusual growths called thyroid nodules form inside the thyroid gland, and early identification of these growths is essential for the diagnosis of thyroid conditions and possibly cancerous growths.

The study makes use of convolutional neural networks (CNNs) to detect thyroid nodules automatically by analyzing medical photographs. Renowned for their remarkable efficacy in image classification assignments, the ResNet50 and VGG16 architectures have been refined and trained on an extensive dataset of thyroid ultrasonography pictures.

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Employing two well-known deep learning models, ResNet50 and VGG16, in a novel way to identify thyroid lesions. The identification of thyroid nodules, which are atypical growths that arise inside the thyroid gland, is of paramount importance in the diagnosis of thyroid conditions and possible cancers.

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