Name
Title | ||
|---|---|---|
Optimized Deep Learning Architecture For The Diagnosis Of Pneumonia Through Chest X-Rays |
Abstract | ||
|---|---|---|
One of the most common exams done in hospitals is the chest radiograph. From results of this exam, many illnesses can be diagnosed such as Pneumonia, which is deadliest illness for children. The main objective of this work is to propose a convolutional neural network model that performs the diagnosis of pneumonia through chest radiographs. The model's proposed architecture is automatically generated through optimization of hyperparameters. Generated models were trained and validated with an image base of chest radiographs presenting cases of viral and bacterial pneumonia. The best architecture found resulted in an accuracy of 95.3% and an AUC of 94% for diagnosing pneumonia, while the best architecture for the classification of type of pneumonia attained an accuracy of 83.1% and AUC of 80%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-27272-2_31 | IMAGE ANALYSIS AND RECOGNITION (ICIAR 2019), PT II |
Keywords | DocType | Volume |
Pneumonia, Chest radiography, Deep neural network, Diagnosis | Conference | 11663 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.43 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Garcez Barros Sousa | 1 | 1 | 0.43 |
| Vandécia Rejane Monteiro Fernandes | 2 | 1 | 0.43 |
| Anselmo C. Paiva | 3 | 379 | 48.88 |