Classification of Breast Lesions Using Mammary Sinograms and Deep Learning

Estefania Ruiz Munoz,Leopoldo Altamirano Robles,Raquel Diaz Hernandez

PATTERN RECOGNITION, MCPR 2024（2024）

Cited 0|Views5

Abstract

Breast cancer is a disease that affects many women worldwide. Therefore, early detection of breast lesions is essential for effective diagnosis and treatment. In this context, digital breast tomosynthesis (DBT) is a promising technique for improving lesion detection compared to conventional mammography. However, image reconstruction can lead to a loss of information. Sinograms provide sufficient information for identifying and localizing lesions while reducing the noise and artifacts associated with the image reconstruction process. In this paper, we propose to employ image processing and deep learning techniques, especially convolutional neural networks (CNN), to identify patterns and extract relevant information from DBT sinograms. The objective is to facilitate diagnosis and treatment through the analysis of sinograms, allowing the identification of patterns and the extraction of useful information for continuous monitoring and evaluation of treatment over time. The results show that the ResNet50 and ResNet18 models have demonstrated a solid performance in classifying breast lesions from sinograms, with outstanding results in accuracy, recall, and F1-score of 94.96%, supporting their efficacy in this task. It is essential to highlight that although there are studies on sinograms in other fields, their application in diagnosing breast lesions has not yet been explored.

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Key words

sinograms,DBT,detection,classification,CNN,breast lesions

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【要点】：本文提出使用数字乳腺断层成像（DBT）的乳腺正弦图和深度学习技术，尤其是卷积神经网络（CNN），来分类乳腺病变，实现高准确率、召回率和F1分数，为乳腺病变的诊断和治疗提供新方法。

【方法】：通过图像处理和深度学习技术，特别是使用ResNet50和ResNet18模型，从DBT正弦图中提取特征并识别模式。

【实验】：研究使用了一个数据集（未明确提及数据集名称），实验结果表明ResNet50和ResNet18模型在乳腺病变分类任务中表现良好，准确率、召回率和F1分数达到了94.96%。