Segmentation for mammography classification utilizing deep convolutional neural network.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-12-18 DOI:10.1186/s12880-024-01510-2
Dip Kumar Saha, Tuhin Hossain, Mejdl Safran, Sultan Alfarhood, M F Mridha, Dunren Che
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Abstract

Background: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently, many deep learning (DL)-based computer-aided diagnosis (CAD) approaches for BC classification have been developed.

Methods: Recently, the transformer model has emerged as a method for overcoming the constraints of convolutional neural networks (CNN). Thus, our primary goal was to determine how well an improved transformer model could distinguish between benign and malignant breast tissues. In this instance, we drew on the Mendeley data repository's INbreast dataset, which includes benign and malignant breast types. Additionally, the segmentation anything model (SAM) method was used to generate the optimized cutoff for region of interest (ROI) extraction from all mammograms. We implemented a successful architecture modification at the bottom layer of a pyramid transformer (PTr) to identify BC from mammography images.

Results: The proposed PTr model using a transfer learning (TL) approach with a segmentation technique achieved the best accuracy of 99.96% for binary classifications with an area under the curve (AUC) score of 99.98%, respectively. We also compared the performance of the proposed model with other transformer model vision transformers (ViT) and DL models, MobileNetV3 and EfficientNetB7, respectively.

Conclusions: In this study, a modified transformer model is proposed for BC prediction and mammography image classification using segmentation approaches. Data segmentation techniques accurately identify the regions affected by BC. Finally, the proposed transformer model accurately classified benign and malignant breast tissues, which is vital for radiologists to guide future treatment.

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基于深度卷积神经网络的乳腺造影分类分割。
背景:早期乳腺癌(BC)的乳房x线摄影诊断在很大程度上依赖于乳房肿块的识别。然而,在早期阶段,确定乳房肿块是良性还是恶性可能是一项挑战。因此,许多基于深度学习(DL)的计算机辅助诊断(CAD)方法被开发出来用于BC分类。方法:最近,变压器模型作为一种克服卷积神经网络(CNN)约束的方法而出现。因此,我们的主要目标是确定改进的变压器模型如何区分乳腺组织的良性和恶性。在本例中,我们利用Mendeley数据存储库的INbreast数据集,其中包括良性和恶性乳房类型。此外,使用分割任意模型(SAM)方法对所有乳房x光片生成感兴趣区域(ROI)提取的优化截止点。我们在金字塔变压器(PTr)的底层实施了成功的结构修改,以从乳房x线摄影图像中识别BC。结果:采用迁移学习(TL)方法和分割技术的PTr模型对曲线下面积(AUC)分数为99.98%的二元分类的准确率达到99.96%。我们还将所提出的模型的性能与其他变压器模型视觉变压器(ViT)和DL模型MobileNetV3和EfficientNetB7进行了比较。结论:在本研究中,提出了一种改进的变压器模型,用于乳腺癌预测和乳房x线摄影图像的分割分类。数据分割技术可以准确地识别受BC影响的区域。最后,本文提出的变压器模型能够准确地区分乳腺组织的良恶性,这对于放射科医生指导未来的治疗至关重要。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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