Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning

Dini Adni Navastara; Fiqey Indriati Eka Sari; Chastine Fatichah; Muchamad Maroqi Abdul Jalil; Mochammad Thaha; Satriyo Dwi Suryantoro; Wahyu Putri Sulistyaning; Mutiara R. Haryati

doi:10.1109/ISRITI60336.2023.10467296

Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning

Dini Adni Navastara
, Fiqey Indriati Eka Sari
, Chastine Fatichah
, Muchamad Maroqi Abdul Jalil
, Mochammad Thaha
, Satriyo Dwi Suryantoro
, Wahyu Putri Sulistyaning
, Mutiara R. Haryati

Institut Teknologi Sepuluh Nopember
Universitas Airlangga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	433-438
Number of pages	6
ISBN (Electronic)	9798350358346
DOIs	https://doi.org/10.1109/ISRITI60336.2023.10467296
Publication status	Published - 2023
Event	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Batam, Indonesia Duration: 11 Dec 2023 → …

Publication series

Name	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding

Conference

Conference	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023
Country/Territory	Indonesia
City	Batam
Period	11/12/23 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Abnormality Detection
Continuous Ambulatory Peritoneal Dialysis (CAPD)
Deep Learning
Effluent Dialysate Image
ResNet

Access to Document

10.1109/ISRITI60336.2023.10467296

Cite this

Navastara, D. A., Indriati Eka Sari, F., Fatichah, C., Maroqi Abdul Jalil, M., Thaha, M., Dwi Suryantoro, S., Putri Sulistyaning, W., & Haryati, M. R. (2023). Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning. In 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding (pp. 433-438). (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISRITI60336.2023.10467296

Navastara, Dini Adni ; Indriati Eka Sari, Fiqey ; Fatichah, Chastine et al. / Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning. 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 433-438 (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding).

@inproceedings{2f8578902a0646e28e784a221c586930,

title = "Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning",

abstract = "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.",

keywords = "Abnormality Detection, Continuous Ambulatory Peritoneal Dialysis (CAPD), Deep Learning, Effluent Dialysate Image, ResNet",

author = "Navastara, \{Dini Adni\} and \{Indriati Eka Sari\}, Fiqey and Chastine Fatichah and \{Maroqi Abdul Jalil\}, Muchamad and Mochammad Thaha and \{Dwi Suryantoro\}, Satriyo and \{Putri Sulistyaning\}, Wahyu and Haryati, \{Mutiara R.\}",

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year = "2023",

doi = "10.1109/ISRITI60336.2023.10467296",

language = "English",

series = "6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "433--438",

booktitle = "6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding",

address = "United States",

}

Navastara, DA, Indriati Eka Sari, F, Fatichah, C, Maroqi Abdul Jalil, M, Thaha, M, Dwi Suryantoro, S, Putri Sulistyaning, W & Haryati, MR 2023, Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning. in 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding, Institute of Electrical and Electronics Engineers Inc., pp. 433-438, 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023, Batam, Indonesia, 11/12/23. https://doi.org/10.1109/ISRITI60336.2023.10467296

Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning. / Navastara, Dini Adni; Indriati Eka Sari, Fiqey; Fatichah, Chastine et al.
6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. p. 433-438 (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

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 - https://www.scopus.com/pages/publications/85190065805

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 -

Navastara DA, Indriati Eka Sari F, Fatichah C, Maroqi Abdul Jalil M, Thaha M, Dwi Suryantoro S et al. Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning. In 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2023. p. 433-438. (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding). doi: 10.1109/ISRITI60336.2023.10467296

Abnormality Detection of Effluent Dialysate Images on Continuous Ambulatory Peritoneal Dialysis Using Deep Learning

Abstract

Publication series

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UN SDGs

Keywords

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