scispace - formally typeset
Journal ArticleDOI

Nipple Segmentation and Localization Using Modified U-Net on Breast Ultrasound Images

Reads0
Chats0
About
This article is published in Journal of Medical Imaging and Health Informatics.The article was published on 2019-12-01. It has received 16 citations till now. The article focuses on the topics: Breast ultrasound.

read more

Citations
More filters
Journal ArticleDOI

A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection

TL;DR: This work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices and confirms that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair, T2, T1C and clinical images.
Journal ArticleDOI

RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks

TL;DR: This paper proposes a Generative Adversarial Network (GAN) based algorithm for segmenting the tumor in Breast Ultrasound images and showcases the shortcomings of CNN, RDA U-Net and other models and how they can be rectified using the WGAN-RDA-UNET model.
Journal ArticleDOI

ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans

TL;DR: The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient, and the proposed model showed excellent segmentation effects.
Journal ArticleDOI

Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning

TL;DR: A workflow and data set of high‐resolution image masks are developed to segment plant tissues in herbarium specimen images and remove background pixels using deep learning.
Posted Content

Deep Learning in Medical Ultrasound Image Segmentation: a Review.

TL;DR: In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first, and the challenges and potential research directions for medical ultrasound image segmentsation are discussed.