Activity Monitoring Systems for Children with Cancer Using the Convolutional Neural Network (CNN) Method

DOI

10.2991/978-94-6463-118-0_51How to use a DOI?

Keywords

Childhood Cancer; Cancer Related Fatigue; Image Processing; Machine Learning; Convolutional Neural Network (CNN)

Abstract

Cancer is one of the killing diseases. Cancer is a condition triggered by the uncontrolled division of abnormal cells in a specific body part. Childhood cancer is a diagnosis of cancer that occurs in children up to the age of 18 years, including children who are still in the womb. Community Care for Child Cancer and Chronic Diseases (KPKAPK) which provides psychological assistance services for families and children, providing services informal education and mentoring of children, providing activities education and recreation for children with cancer, as well as fundraising to help children’s medical expenses from the underprivileged family. The effects of cancer treatment carried out by children with cancer can be in the form of diarrhea, nausea, vomiting and fatigue. Cancer-Related Fatigue (CRF) is a common side effect in cancer patients in those experiencing cytotoxic chemotherapy, radiation therapy, bone marrow transplantation, or biologic response modifier Cancer-Related Fatigue (CRF) is a common side effect experienced by cancer patients undergoing cytotoxic chemotherapy, radiation treatment, bone marrow transplantation, or biologic response clarifier therapies.The urgency of this research emerged as a form of social care and support for children with cancer who take shelter in halfway houses, support patients in handling this distressing condition and remaining as active in life as possible, also support for the volunteers involved in the shelter. Researcher have designed a technology that will help the volunteers in monitoring the activities of children with cancer patients at shelter, utilizing intelligent system technology that can identify image recorded by the camera and processes it with Image Processing based on Machine Learning embedded in it Convolutional Neural Network (CNN) Algorithm.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. D. Kusumanto
AU  - M. Nawawi
AU  - Ekawati Prihatini
AU  - Yessi Marniati
AU  - Nyayu Latifah Husni
AU  - Ade Silvia Handayani
PY  - 2023
DA  - 2023/06/26
TI  - Activity Monitoring Systems for Children with Cancer Using the Convolutional Neural Network (CNN) Method
BT  - Proceedings of the 6th FIRST 2022 International Conference (FIRST-ESCSI-22)
PB  - Atlantis Press
SP  - 493
EP  - 500
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-118-0_51
DO  - 10.2991/978-94-6463-118-0_51
ID  - Kusumanto2023
ER  -