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

TLDR

Computer‑aided diagnosis of breast lesions in ultrasound images has attracted attention, but traditional handcrafted‑feature methods struggle due to breast anatomy and image noise, prompting a shift toward deep learning for improved detection and classification. The study systematically evaluates state‑of‑the‑art object detection and classification methods for breast‑lesion CAD. The authors collected a new dataset of 1,043 breast‑lesion ultrasound images annotated by clinicians and evaluated multiple deep‑learning architectures on it. SSD300 achieved the highest detection metrics, DenseNet performed best for classification, and both advanced CNN frameworks and ImageNet transfer learning significantly improved breast‑lesion detection and classification.

Abstract

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.