Reduksi Noise Pada Citra Ultrasound Image Menggunakan Deep Learning. (Noise Reduction in Ultrasound Images Using Deep Learning).

Heryana, Salsabilla Putri (2024) Reduksi Noise Pada Citra Ultrasound Image Menggunakan Deep Learning. (Noise Reduction in Ultrasound Images Using Deep Learning). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

One important step towards accurate diagnosis is improving the quality of breast ultrasound images. Noise is a major problem in ultrasound image processing, as it can obscure important details and interfere with medical analysis. The results of this study show that Denoising Convolutional Neural Network (DnCNN). A convolutional neural network specialized for reducing noise in breast ultrasound images. This research uses DnCNN to process ultrasound images that still contain noise, and the research findings show that DnCNN can reduce various types of noise without destroying important details in the image, which results in a clearer and sharper image. The analysis was performed by comparing images before and after denoising Pratt's Figure Of Merit (FOM) matrix. The quality evaluation results show a Figure Of Merit Pratt (FOM) value of 85%, indicating that this method preserves details by 85%. using DnCNN. When processing breast ultrasound images it can improve image quality, reduce noise, and preserve important details. This suggests that DnCNN can aid more accurate and effective medical diagnosis.

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