TY - GEN
T1 - Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning
AU - Navastara, Dini Adni
AU - Indriati Eka Sari, Fiqey
AU - Fatichah, Chastine
AU - Maroqi Abdul Jalil, Muchamad
AU - Thaha, Mochammad
AU - Dwi Suryantoro, Satriyo
AU - Putri Sulistyaning, Wahyu
AU - Haryati, Mutiara R.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Continuous Ambulatory Peritoneal Dialysis (CAPD) offers independent dialysis for chronic kidney disease (CKD) patients in Indonesia, enhancing quality of life. However, high mortality risks due to complications prompted by negligence and technical errors persist. In this research, an innovative contribution was suggested: designing a system to detect complications related to CAPD based on abnormality detection of effluent dialysate images using deep learning. The images are harnessed as input for the deep learning model, employing transfer learning techniques atop pre-trained models such as InceptionV3, ResNet50, EfficientNetB7, and MobileNetV2. After this, the model is refined by incorporating supplementary layers, and an array of experiments encompassing diverse dataset resampling scenarios and augmentation strategies are conducted. Empirical findings underscore that the optimal model manifests with ResNet50 under precise circumstances, yielding a recall rate of 75.00% and an f1-score of 78.95% on the test dataset.
AB - Continuous Ambulatory Peritoneal Dialysis (CAPD) offers independent dialysis for chronic kidney disease (CKD) patients in Indonesia, enhancing quality of life. However, high mortality risks due to complications prompted by negligence and technical errors persist. In this research, an innovative contribution was suggested: designing a system to detect complications related to CAPD based on abnormality detection of effluent dialysate images using deep learning. The images are harnessed as input for the deep learning model, employing transfer learning techniques atop pre-trained models such as InceptionV3, ResNet50, EfficientNetB7, and MobileNetV2. After this, the model is refined by incorporating supplementary layers, and an array of experiments encompassing diverse dataset resampling scenarios and augmentation strategies are conducted. Empirical findings underscore that the optimal model manifests with ResNet50 under precise circumstances, yielding a recall rate of 75.00% and an f1-score of 78.95% on the test dataset.
KW - Abnormality Detection
KW - Continuous Ambulatory Peritoneal Dialysis (CAPD)
KW - Deep Learning
KW - Effluent Dialysate Image
KW - ResNet
UR - http://www.scopus.com/inward/record.url?scp=85190065805&partnerID=8YFLogxK
U2 - 10.1109/ISRITI60336.2023.10467296
DO - 10.1109/ISRITI60336.2023.10467296
M3 - Conference contribution
AN - SCOPUS:85190065805
T3 - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
SP - 433
EP - 438
BT - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023
Y2 - 11 December 2023
ER -
