Single shot detector application for image disease localization
pawar, s.; Stanam, A.; Chopade, R.
Show abstract
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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MultiHeadGAN: A Deep Learning Method for Low Contrast Retinal Pigment Epithelium Cells Segmentation in Fluorescent Flatmount Microscopy Images 95%
- BenchXAI: Comprehensive Benchmarking of Post-hoc Explainable AI Methods on Multi-Modal Biomedical Data 94%
- The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis 94%
Similar papers in this journal
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 97%
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 96%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 96%
Similar papers in this journal
- SN-FPN: Self-attention Nested Feature Pyramid Network for Digital Pathology Image Segmentation 97%
- A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning 96%
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.