The Basic Classification

An implementation of multi-layer perceptron for classifying thyroid disease dataset

An innovative approach to detect thyroid nodules using two popular deep learning models, ResNet50 and VGG16. Thyroid nodules are abnormal growths that develop within the thyroid gland, and their early detection plays a crucial role in diagnosing thyroid disorders and potential malignancies.

Code of Thyroid Disease Detection Project, which we can use to detect the thyroid disease of an individual.

🦋Explore our Thyroid Disease Detection Project, where cutting-edge machine learning 🤖 meets medical diagnostics. 📊🔬 Join us in revolutionizing healthcare with AI, making strides towards a healthier future! 🌟💉

Thyroid Syndrome Detection using Machine Learning Algorithms 🔬

Machine learning using python. This system predicts if a person has thyroid or not on the basis of certain inputs taken from the patient.

Created Thyroid Detection App using Streamlit

Diagnostic Support System for Euthyroid Sick Syndrome based on Machine Learning Algorithms Approches

A thyroid disease detection, Amazon Sagemaker using Scikit-learn Pipeline (StandardScalar & SVM)

"Machine learning project to predict thyroid diseases based on patient data."

The most common thyroid disorder is hypothyroidism. Hypo- means deficient or under(active), so hypothyroidism is a condition in which the thyroid gland is underperforming or producing too little thyroid hormone.. Recognizing the symptoms of hypothyroidism is extremely important.

Accuracy- 98.7% on predicting the presence or absence of Thyroid

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.