Skip to main content

01.12.2019 | Research article | Ausgabe 1/2019 Open Access

BMC Medical Imaging 1/2019

An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures

BMC Medical Imaging > Ausgabe 1/2019
Zhantao Cao, Lixin Duan, Guowu Yang, Ting Yue, Qin Chen
Wichtige Hinweise

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



Computer-aided diagnosis (CAD) in the medical field has received more and more attention in recent years. One important CAD application is to detect and classify breast lesions in ultrasound images. Traditionally, the process of CAD for breast lesions classification is mainly composed of two separated steps: i) locate the lesion region of interests (ROI); ii) classify the located region of interests (ROI) to see if they are benign or not. However, due to the complex structure of breast and the existence of noise in the ultrasound images, traditional handcrafted feature based methods usually can not achieve satisfactory result.


With the recent advance of deep learning, the performance of object detection and classification has been boosted to a great extent. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection and classification methods for breast lesions CAD. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. We evaluate different deep learning architectures and conduct comprehensive experiments on our newly collected dataset.


For the lesion regions detecting task, Single Shot MultiBox Detector with the input size as 300×300 (SSD300) achieves the best performance in terms of average precision rate (APR), average recall rate (ARR) and F1 score. For the classification task, DenseNet is more suitable for our problems.


Our experiments reveal that better and more efficient detection and convolutional neural network (CNN) frameworks is one important factor for better performance of detecting and classification task of the breast lesion. Another significant factor for improving the performance of detecting and classification task, which is transfer learning from the large-scale annotated ImageNet to classify breast lesion.
Über diesen Artikel

Weitere Artikel der Ausgabe 1/2019

BMC Medical Imaging 1/2019 Zur Ausgabe

Neu im Fachgebiet Radiologie

Mail Icon II Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Radiologie und bleiben Sie gut informiert – ganz bequem per eMail.