Comprehensive characterization of the Lidar TF Mini sensor for its potential use in early breast cancer detection, employing a data-driven approach
Abstract
Breast cancer is a leading cause of death among women. Early detection helps prevent the spread of cancer to other organs, thereby reducing serious risks. This characterization aims to determine the potential of the TF Mini Lidar sensor if used as an early detection tool for breast cancer. Basically, this characterization uses a data-based approach. The method used in this study is to position the TFmini and HC-SR04 sensors in a fixed and stable position, after which the reflective object is positioned at a predetermined distance according to the distance to be used when detecting breast cancer, which is at a distance of 51-100 cm. PLX daq version 2.11 was used to facilitate data collection. The characterization of this sensor is based on the standard deviation, relative standard deviation, error and accuracy. This study concludes that the TFmini lidar sensor has a high potential to be used in breast cancer detection devices as a contour detector. This is in accordance with the measurement accuracy value of the tfmini sensor of 99.87%. However, additional sensors, such as cameras, are needed to obtain better contours and visual images.
Keywords
Full Text:
PDF (53-64)References
Abdulkhaleq, N. I., Hasan, I. J., & Salih, N. A. J. (2020). Investigating the resolution ability of the HC-SRO4 ultrasonic sensor. IOP Conference Series: Materials Science and Engineering, 745(1). https://doi.org/10.1088/1757-899X/745/1/012043
Aina Escalera Mendo. (2022). Investigation into the principles of LiDAR and use in modern electronic systems [Universitat Politècnica de Catalunya]. http://hdl.handle.net/2117/380638
Aldhaeebi, M. A., Alzoubi, K., Almoneef, T. S., Bamatra, S. M., Attia, H., & Ramahi, O. M. (2020). Review of microwaves techniques for breast cancer detection. In Sensors (Switzerland) (Vol. 20, Issue 8). MDPI AG. https://doi.org/10.3390/s20082390
Arsyad Cahya Subrata, Sirajuddin, M. M., Salsabila, S. R., Ibad, I., Prasetyo, E., & Yusmianto, F. (2024). Low-Cost Early Detection Device for Breast Cancer based on Skin Surface Temperature. IT Journal Research and Development, 9(1), 27–37. https://doi.org/10.25299/itjrd.2024.16034
Braik, R., Elmadani, A., Idrissi, M., Achaoui, Y., & Jakjoud, H. (2024). Generation and propagation of acoustic solitons in a periodic waveguide of air-water metamaterials. New Journal of Physics, 26(2). https://doi.org/10.1088/1367-2630/ad23a7
Busaeed, S., Mehmood, R., Katib, I., & Corchado, J. M. (2022). LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino. Electronics (Switzerland), 11(7). https://doi.org/10.3390/electronics11071076
Castiblanco, F. A., Lee, B., Natraj Arun, A., Balmos, A., Jha, S., Krogmeier, J. V, Love, D. J., & Buckmaster, D. (2024). OATSMobile: A Data Hub for Underground Sensor Communications and Rural IoT.
Costa, J. P., Baptista, M., Amantes, A., & Conceição, T. (2024). Validation of a learning progression for sound propagation in air. Eurasia Journal of Mathematics, Science and Technology Education, 20(7). https://doi.org/10.29333/ejmste/14704
Crislia Ardith Wulandari, & Rusmini. (2020). VALIDITAS TEORITIS LKPD UNTUK MEREDUKSI MISKONSEPSI PADA MATERI STOIKIOMETRI MENGGUNAKAN MODEL PEMBELAJARAN ECIRR UNTUK KELAS X SMA. UNESA Journal of Chemical Education, 9(2), 265–274.
Diniz, V., Santana, F., Rogério, ;, Salustiano, E., & De Oliveira Tiezzi, R. (2024). Development and Calibration of a Low-Cost LIDAR Sensor for Water Level Measurements. https://ssrn.com/abstract=4752716
Elsheakh, D. N., Fahmy, O. M., Farouk, M., Ezzat, K., & Eldamak, A. R. (2024). An Early Breast Cancer Detection by Using Wearable Flexible Sensors and Artificial Intelligent. IEEE Access, 12, 48511–48529. https://doi.org/10.1109/ACCESS.2024.3380453
Elsheakh, D. N., Mohamed, R. A., Fahmy, O. M., Ezzat, K., & Eldamak, A. R. (2023a). Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors. Biosensors, 13(1), 87. https://doi.org/10.3390/bios13010087
Elsheakh, D. N., Mohamed, R. A., Fahmy, O. M., Ezzat, K., & Eldamak, A. R. (2023b). Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors. Biosensors, 13(1), 87. https://doi.org/10.3390/bios13010087
Fakhri, S., Mardiati, R., Mulyana, E., & Priatna, T. (2020). Prototype Design for Object Coordinate Detection using RP LIDAR Concept. 2020 6th International Conference on Wireless and Telematics (ICWT), 1–6. https://doi.org/10.1109/ICWT50448.2020.9243654
Hamza, M. N., Islam, M. T., & Koziel, S. (2024). Advanced sensor for non-invasive breast cancer and brain cancer diagnosis using antenna array with metamaterial-based AMC. Engineering Science and Technology, an International Journal, 56. https://doi.org/10.1016/j.jestch.2024.101779
Hamza, M. N., Koziel, S., & Pietrenko-Dabrowska, A. (2024). Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-67103-9
Holzhüter, H., Bödewadt, J., Bayesteh, S., Aschinger, A., & Blume, H. (2023). Technical concepts of automotive LiDAR sensors: a review. Optical Engineering, 62(03). https://doi.org/10.1117/1.OE.62.3.031213
Huang, P.-Y., Jiang, B.-Y., Chen, H.-J., Xu, J.-Y., Wang, K., Zhu, C.-Y., Hu, X.-Y., Li, D., Zhen, L., Zhou, F.-C., Qin, J.-K., & Xu, C.-Y. (2023). Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction. Nature Communications, 14(1), 6736. https://doi.org/10.1038/s41467-023-42488-9
Huggins, E. R. (2000). Physics 2000 Geometrical Optics.
Junior, S., & Lima, and. (2024). Comparative Analysis of Cameras for ArUco Marker Recognition in Unmanned Aerial Vehicles. http://www.imavs.org/
Li, N., Ho, C. P., Xue, J., Lim, L. W., Chen, G., Fu, Y. H., & Lee, L. Y. T. (2022). A Progress Review on Solid‐State LiDAR and Nanophotonics‐Based LiDAR Sensors. Laser & Photonics Reviews, 16(11). https://doi.org/10.1002/lpor.202100511
Mittapally, S. K., & Bollam, R. T. (2024). March 2024 Revised 1.pdf#page=3.
Mutava Gabriel, M., & Paul Kuria, K. (2020). Arduino Uno, Ultrasonic Sensor HC-SR04 Motion Detector with Display of Distance in the LCD. www.ijert.org
Nur Aziz, F., & Zakarijah, M. (2022). TF-Mini LiDAR Sensor Performance Analysis for Distance Measurement. In Jurnal Nasional Teknik Elektro dan Teknologi Informasi | (Vol. 11, Issue 3).
Pain, H. (2005). The Physics of Vibrations and Waves 6th ed (6th ed.). John Wiley and Sons Ltd.
Pedrotti, F. L., Pedrotti, L. M., & Pedrotti, L. S. (2017). Introduction to Optics. In Introduction to Optics (Third Edit). https://doi.org/10.1017/9781108552493
Pratap, A., & Rangarej, S. (2024, January 16). Bi-Directional Adjustable Holder for LiDAR Sensor. https://doi.org/10.4271/2024-26-0024
Prayoga, D. B., Wiyono, A., Syariffuddien Zuhrie, M., & Rakhmawati, L. (2024). Sistem Positioning pada Wahana Multicopter Menggunakan Metode Simultaneous Localization and Mapping (SLAM) Berbasis LiDAR dan Inertial Measurement Unit (IMU) 191 Sistem Positioning pada Wahana Multicopter Menggunakan Metode Simultaneous Localization and Mapping (SLAM) Berbasis LiDAR dan Inertial Measurement Unit (IMU).
Prihatini, E., Ismail, R., Rahayu, I. S., & Saputri, E. D. (2024). Pengembangan Sistem Alat Ukur Sudut Kontak dengan Metode Optical Contact Angle. In Jurnal Pengelolaan Laboratorium Pendidikan (Vol. 6, Issue 1).
Purba, A. E. T., & Simanjuntak, E. H. (2019). Efektivitas Pendidikan Kesehatan Sadari terhadap Peningkatan Pengetahuan dan Sikap Wus tentang Deteksi Dini Kanker Payudara. Jurnal Bidan Komunitas, 2(3), 160. https://doi.org/10.33085/jbk.v2i3.4476
Santoso, I. H., & Irawan, A. I. (2022). Analisis Perbandingan Kinerja Sensor Jarak HC-SR04 dan GP2Y0A21YK Dengan Menggunakan Thingspeak dan Wireshark. Jurnal Rekayasa Elektrika, 18(1). https://doi.org/10.17529/jre.v18i1.23359
Seth, A., James, A., Kuantama, E., Mukhopadhyay, S., & Han, R. (2023). Drone High-Rise Aerial Delivery with Vertical Grid Screening. Drones, 7(5). https://doi.org/10.3390/drones7050300
Shanker, mc. (2023). Classification of Breast Cancer in Mammograms Using an Optimized Hybrid Deep Learning Models and Feature Fusion Techniques. ResearchSquare. https://doi.org/10.21203/rs.3.rs-2537277/v1
Silva Cotta, J. L., Rakoczy, J., & Gutierrez, H. (2024). Precision landing comparison between smartphone video guidance sensor and IRLock by hardware-in-the-loop emulation. CEAS Space Journal, 16(4), 475–489. https://doi.org/10.1007/s12567-023-00518-8
Wang, L. (2023). Microwave Imaging and Sensing Techniques for Breast Cancer Detection. Micromachines, 14(7), 1462. https://doi.org/10.3390/mi14071462
Widyawati. (2022, February 2). Kanker Payudara Paling Banyak di Indonesia, Kemenkes Targetkan Pemerataan Layanan Kesehatan. Kementerian Kesehatan Republik Indonesia.
Yustiana Olfah, Ni Ketut Mendri, & Atik Badi’ah. (2019). Kanker Payudara & Sadari (978th-602nd-17607th-3rd–4th ed.). nuha medika.
DOI: http://dx.doi.org/10.30870/gravity.v11i1.28900
Refbacks
- There are currently no refbacks.
Gravity has been indexed by:
_Kecil1.jpg)
| |
      |
| Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika is publihed by Department of Physics Education, Universitas Sultan Ageng Tirtayasa jointly with Physical Society of Indonesia (PSI) | |
 | Gravity : Jurnal Ilmiah Penelitian dan Pembelajaran Fisika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License Copyright © 2020, Gravity: Jurnal Ilmiah Penelitian dan Pembelajaran Fisika. |
