Detection of thyroid stages classification by convolutional neural network techniques

Abstract: Thyroid disease diagnosis and stage classification are critical tasks in medical imaging due to their direct impact on patient treatment and management. Conventional diagnostic approaches based on manual interpretation of thyroid ultrasound images are time-consuming and prone to human error. To address these limitations, this paper presents an automated thyroid stage classification framework using Convolutional Neural Network (CNN) techniques.The proposed work is developed by taking an existing deep learning-based thyroid detection model as the base paper. Approximately 70% of the methodology is derived from the base paper, including image preprocessing concepts and deep feature extraction principles. The remaining 30% represents the proposed project contribution, where the system architecture is simplified and optimized using a CNN-focused approach for effective thyroid stage classification. The methodology involves preprocessing of thyroid ultrasound images to enhance image quality, followed by CNN-based automatic feature extraction and classification into different thyroid stages such as normal and abnormal. Deep learning technology is employed to eliminate manual feature engineering and improve classification performance. Experimental evaluation demonstrates that the proposed CNN-based model provides reliable accuracy and efficient classification compared to traditional diagnostic methods.The results indicate that CNN techniques are effective for thyroid stage classification and can be utilized as a supportive decision-making tool in clinical environments.

Keywords: Thyroid disease detection, thyroid stage classification, convolutional neural network (CNN), medical image classification, ultrasound image processing, deep learning.