Desarrollo de un clasificador basado en redes neuronales para la detección de escoliosis en imagen RX de columna.
Medicine has grown considerably with technology in recent years, tools such as Machine Learning in the medical area are some of the most technical that represent a great benefit for the prevention and diagnosis of pathology in patients. In this work, a convolutional neural network consisting of two...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Trabajo de grado (Pregrado y/o Especialización) |
| Language: | spa |
| Published: |
Universidad Antonio Nariño
2022
|
| Subjects: | |
| Online Access: | http://repositorio.uan.edu.co/handle/123456789/6085 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Medicine has grown considerably with technology in recent years, tools such as
Machine Learning in the medical area are some of the most technical that represent a great
benefit for the prevention and diagnosis of pathology in patients. In this work, a convolutional
neural network consisting of two convolution layers and a feedforward network with 128
neurons and an output layer with three neurons is implemented in Python for the
classification of scoliosis type C and S and healthy patients from RX images. of column of a
Kaggle database and a proprietary database obtained from the Sagrado Corazón de Jesús
Hospital, with the purpose that this algorithm serves as support to physiotherapists and
orthopedists in decision making and that it can be carried out in a shorter time.
The results obtained during the development of the tests indicate that the tool has an
acceptable degree of accuracy compared to other articles such as "Development and
Validation of Deep Learning Algorithms For Scoliosis Screening Using Back Images" Which
Has An Accuracy Over 80% Using Back Images. Real-time patients or photographs that
included a total of 3240 images for training (including 1029 men and 2211 women) and for
validation a total of 400 images (300 images with scoliosis and 100 of healthy patients),
however the implemented classifier In this work, a limited sample of images was trained and
validated (150 images, divided into 75 images for training and 75 images for testing),
obtaining a success rate of 94%, which can be a support tool for the diagnosis of the type of
scoliosis in an automated way, unlike conventional methods |
|---|
