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Clinical procedures, which require a large number of personnel and medical resources, receive the majority of the current focus on thyroid nodule diagnosis. An automated thyroid ultrasound nodule identification system is built using image texture data and convolutional neural networks in this study. The following are the major phases: The underlying stages in building a ultrasound thyroid knob dataset incorporate gathering positive and negative examples, normalizing pictures, and portioning the knob region. Second, a texture features model is built by selecting features, reducing the dimensionality of the data, and extracting texture features. Third, deep neural networks in move learning are utilized to create an element model of the knob in an image. The convolutional brain network highlight model and the surface component model were combined to create the brand-new knob include model known as the Feature Fusion Network. The Feature Fusion Network is used to prepare and improve performance over a single organization in order to create a demonstrative model for deep neural networks that can adapt to a variety of knob features. 1874 clinical ultrasonography thyroid knobs were gathered for this investigation. The musical normal F-score considering Accuracy and Review is utilized as an assessment metric. With an F-score of 92.52 percent, the study’s findings suggest that the Element Combination Organization can differentiate between benign and harmful thyroid knobs. As far as execution, this methodology performs better compared to standard ML procedures and convolutional neural networks.