PT  - JOURNAL ARTICLE
AU  - Rushikesh Chopade
AU  - Aditya Stanam
AU  - Shrikant Pawar
TI  - Single shot detector application for image disease localization
AID  - 10.1101/2021.09.21.461307
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.09.21.461307
4099  - http://biorxiv.org/content/early/2021/09/24/2021.09.21.461307.short
4100  - http://biorxiv.org/content/early/2021/09/24/2021.09.21.461307.full
AB  - Bounding box algorithms are useful in localization of image patterns. Recently, utilization of convolutional neural networks on X-ray images has proven a promising disease prediction technique. However, pattern localization over prediction has always been a challenging task with inconsistent coordinates, sizes, resolution and capture positions of an image. Several model architectures like Fast R-CNN, Faster R-CNN, Histogram of Oriented Gradients (HOG), You only look once (YOLO), Region-based Convolutional Neural Networks (R-CNN), Region-based Fully Convolutional Networks (R-FCN), Single Shot Detector (SSD), etc. are used for object detection and localization in modern-day computer vision applications. SSD and region-based detectors like Fast R-CNN or Faster R-CNN are very similar in design and implementation, but SSD have shown to work efficiently with larger frames per second (FPS) and lower resolution images. In this article, we present a unique approach of SSD with a VGG-16 network as a backbone for feature detection of bounding box algorithm to predict the location of an anomaly within chest X-ray image.Competing Interest StatementThe authors have declared no competing interest.