TY - GEN
T1 - Classification of thorax diseases using deep learning
AU - Aulia, Sofi N.
AU - Haekal, Mohammad
AU - Endarko, Endarko
N1 - Publisher Copyright:
© 2023 Author(s).
PY - 2023/5/9
Y1 - 2023/5/9
N2 - Deep Learning for analyzing medical images in thoracic diseases is considered necessary in radiology. The thorax examination is the most frequently performed examination in routine examinations. Analytical methods that can explain medical image analysis finding morphological patterns or textures in images are urgently needed. Currently, the interpretation of the thorax is carried out manually by radiologists based on thorax images that are influenced by the cognitive abilities and subjective experience of radiologists, thus allowing human error to occur in reading or analyzing. This study aimed to classify chest diseases such as Edema, Pneumonia, and Pneumothorax using a Convolutional NeurXR) image data used to determine chest disease are normal, edema, pneumonia, and pneumothorax data of 1487, 1022, 1374, and 1128 with an image size of 200×200 pixels. This study also analyzed optimizers RMSprop, Adadelta, Adagrad, Adamax, and Adam in the classification of thorax disease for the CNN algorithm. The results showed that Adam is the best optimizing parameter and can show good accuracy of more than 98% for the classification of thorax diseases such as Edema, Pneumonia, and Pneumothorax with epochs equal to 10.
AB - Deep Learning for analyzing medical images in thoracic diseases is considered necessary in radiology. The thorax examination is the most frequently performed examination in routine examinations. Analytical methods that can explain medical image analysis finding morphological patterns or textures in images are urgently needed. Currently, the interpretation of the thorax is carried out manually by radiologists based on thorax images that are influenced by the cognitive abilities and subjective experience of radiologists, thus allowing human error to occur in reading or analyzing. This study aimed to classify chest diseases such as Edema, Pneumonia, and Pneumothorax using a Convolutional NeurXR) image data used to determine chest disease are normal, edema, pneumonia, and pneumothorax data of 1487, 1022, 1374, and 1128 with an image size of 200×200 pixels. This study also analyzed optimizers RMSprop, Adadelta, Adagrad, Adamax, and Adam in the classification of thorax disease for the CNN algorithm. The results showed that Adam is the best optimizing parameter and can show good accuracy of more than 98% for the classification of thorax diseases such as Edema, Pneumonia, and Pneumothorax with epochs equal to 10.
UR - http://www.scopus.com/inward/record.url?scp=85160401203&partnerID=8YFLogxK
U2 - 10.1063/5.0114172
DO - 10.1063/5.0114172
M3 - Conference contribution
AN - SCOPUS:85160401203
T3 - AIP Conference Proceedings
BT - 2nd International Symposium on Physics and Applications 2021
A2 - Asih, Retno
A2 - Nasori, null
A2 - Saifuddin, null
A2 - Haekal, Muhammad
PB - American Institute of Physics Inc.
T2 - 2nd International Symposium on Physics and Applications 2021, ISPA 2021
Y2 - 13 November 2021 through 14 November 2021
ER -