Name
Abstract | ||
|---|---|---|
The field of computer vision has had exponential progress in a wide range of applications due to the use of deep learning and especially the existence of large annotated image data sets [1]. Significant improvements have been shown in the performance of problems previously considered difficult, such as object recognition, detection and segmentation over approaches based on obtaining the characteristics of the image by hand [2]. This article presents a novel method for the classification of chest diseases in the standard and widely used data set ChestX-ray8, which contains more than 100,000 front view images with 8 diseases. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-53036-5_16 | DCAI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Silva | 1 | 0 | 1.69 |
| Jack Zilberman | 2 | 0 | 0.68 |
| Yisel Pinillos-Patiño | 3 | 0 | 0.34 |
| Noel Varela | 4 | 0 | 2.37 |
| Omar Bonerge Pineda Lezama | 5 | 1 | 12.86 |