Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound


Automated breast ultrasound, often known as ABUS, is a novel and promising screening technique for the breasts. Instead of relying on the operator to capture images as is the case with traditional 2D B-mode ultrasound, ABUS is able to capture images without the need for a human operator. However, the process of examining ABUS photographs is extremely time-consuming, and mistakes may be made due to oversight. In order to speed up the review process while still achieving high detection sensitivity and low false positives, we've developed a new 3D convolutional network for use in this study (FPs). By utilising multi-layer features efficiently, we present a densely deep supervision method that significantly increases detection sensitivity. To help distinguish between cancer and non-cancer at the voxel level, we propose that a threshold loss be used, which can yield high sensitivity and low false positives. Data from 219 patients, 614 ABUS volumes, 745 cancer regions, and 144 healthy women, all totaling over 900 volumes, has been used to verify the effectiveness of our network. With 0.84 FP per volume, extensive testing has shown that our approach is 95 percent sensitive. The suggested network offers a breast cancer detection strategy using ABUS that maintains high sensitivity with low false positives. It's an effective system. If you're interested, you may get the source code at code.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : On the Scalability of Machine-Learning Algorithms for Breast Cancer Prediction in Big Data Context ABSTRACT: Data has grown at an exponential rate as a result of recent developments in information technology, ushering
PROJECT TITLE : Deep Neural Networks Improve Radiologists Performance in Breast Cancer Screening ABSTRACT: To classify mammograms for breast cancer screening, we developed a deep convolutional neural network that was trained and
PROJECT TITLE :Her2Net A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation - 2018ABSTRACT:We tend to gift an economical deep learning framework for identifying,
PROJECT TITLE : Lung cancer survival prediction from pathological images and Genetic data - an integration study - 2016 ABSTRACT: In this paper, we tend to have proposed a framework for lung cancer survival prediction by integrating
PROJECT TITLE : Locality sensitive deep learning for detection and Classification of nuclei in routine colon cancer Histology images - 2016 ABSTRACT: Detection and classification of cell nuclei in histopathology images of cancerous

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry