Deteksi Dini Kanker Payudara Menggunakan Citra Ultrasonografi Berbasis Convolutional Neural Networks dan Particle Swarm Optimization
-
Serina Azahra
*
Sekolah Tinggi Ilmu Kesehatan Semarang, Semarang,
Indonesia
- Pramesti Kusumaningtyas Sekolah Tinggi Ilmu Kesehatan Semarang, Semarang, Indonesia
- Mohammad Rofi'i Sekolah Tinggi Ilmu Kesehatan Semarang, Semarang, Indonesia (*) Corresponding Author
Abstract
The increasing prevalence of breast cancer, one of the cancers with the highest incidence rate in the world, demands the development of effective early detection methods to increase patients' chances of recovery and reduce treatment costs. The main challenge in early detection of breast cancer is the identification of early symptoms that are often not felt, so many cases are only detected at an advanced stage. This study aims to develop a breast cancer early detection model using ultrasound images based on the convolutional neural networks (CNN) artificial neural network method optimized with the particle swarm optimization (PSO) algorithm. CNN is used to extract complex features from medical images, while PSO optimizes model parameters to improve accuracy. The dataset consists of 1,607 images classified into normal, benign, and malignant categories, through the process of model training and validation. The results showed that the integration of particle swarm optimization and convolutional neural networks resulted in an accuracy of 90.67%, higher than the convolutional neural networks method alone at 87.33%. In addition, the CNN-PSO model also excels in precision, sensitivity, and F1-score value. This research provides an effective diagnostic technology solution for primary healthcare facilities, with implications for improved early detection and reduced delayed diagnosis. This technology can be applied more widely through training of health workers and equitable distribution of diagnostic devices to improve service accessibility.
Downloads
References
Z. N. Shidqi, L. D. Saraswati, N. Kusariana, D. Sutiningsih, and A. Udijono, “Faktor-Faktor Keterlambatan Diagnosis Kanker pada Pasien Kanker Payudara: Systemic Review,” J. Epidemiol. Kesehat. Komunitas, vol. 7, no. 2, pp. 471–481, 2022.
A. Herawati, S. Rijal, and A. S. F. Arsal, “Karakteristik Kanker Payudara,” FAKUMI Med. J. J. Mhs. Kedokt., vol. 1, no. 1, pp. 44–53, 2021, doi: https://doi.org/10.33096/fmj.v1i1.8.
E. Ernawati, S. Sumarmi, M. Mantasia, and R. Nuryana, “Gambaran Pengetahuan Dan Sikap Remaja Putri Sebelum Dan Sesudah Penyuluhan Tentang Periksa Payudara Sendiri (Sadari),” Borobudur Nurs. Rev., vol. 2, no. 2, pp. 127–134, 2022, doi: 10.31603/bnur.7811.
B. Anggriani, R. J. Sitorus, R. Flora, and Octariyana, “Perempuan dan Penyakit Keganasan (Kanker Payudara dan Kanker Serviks),” e-SEHAD Sci. Environ. Heal. Dis., vol. 3, no. 1, pp. 131–142, 2022.
A. Rahmawati, I. U. Sari, and H. Sumarti, “Klasifikasi Tumor Payudara Jinak dan Ganas pada Citra Ultrasonografi ( USG ) Berdasarkan Karakteristik Tekstur Menggunakan Metode Random Forest.,” pp. 38–44, 2024, doi: https://doi.org/10.20884/1.jtf.2024.7.1.10997.
I. L. N. Qadar, N. H. Aprianto, P. Supriyanto, and A. A. A. Diartama, “Teknik Pemeriksaan Ultrasonografi Panggul dengan Klinis Kista Ovarium di Rumah Sakit Umum Daerah Cengkareng,” J. Ilmu Kedokt. dan Kesehat., vol. 10, no. 11, pp. 3141–3147, 2023.
E. I. H. . Manik, “Analisis Perbandingan Kinerja Jaringan Neural Konvolusi (CNN) dalam Pengenalan Pola pada Citra Medis,” 2023, doi: http://dx.doi.org/10.13140/RG.2.2.31372.69762.
R. F. S. A. Simamora and Sutarman, “Penaksiran Parameter Regresi Nonlinear Menggunakan Particle Swarm Optimization dan Genetic Algorithm,” Leibniz J. Mat., vol. 4, no. 2, pp. 71–83, 2024.
R. Shalehuddin Albawani, F. Tri Anggraeny, and M. Muharrom Al Haromainy, “Implementasi Seblock Pada Klasifikasi Citra Penyakit Mata Manusia Dengan Arsitektur Mobilenetv3-Small,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 1123–1128, 2024, doi: 10.36040/jati.v8i1.8916.
C. R. Fatmah, F. Indriyani, and I. R. Rahadjeng, “Klasifikasi Tumor Otak Berbasis Magnetic Resonance Imaging Menggunakan Algoritma Convolutional Neural Network,” Digit. Digit. Transform. Technol., vol. 3, no. 2, pp. 918–924, 2023, doi: https://doi.org/10.47709/briliance.vxix.xxxx.
F. A. A. Harahap, R. M. Sinaga, K. Arifin, and K. Saputra, “Implementasi Algoritma Convolutional Neural Network untuk Mendeteksi Penyakit Ginjal,” JTIKA J. Teknol. Informasi, Komput. dan Apl., vol. 4, no. 2, 2022.
R. J. L. Melatisudra, S. Utomo, S. Sutjiningtyas, and Hernawati, “Implementasi Pengenalan Ekspresi Wajah dengan Menggunakan Metode Convolutional Neural Networkdan OpenCVBerbasis Webcam,” J. Comput. Syst. Informatics, vol. 6, no. 1, pp. 339–348, 2024, doi: https://doi.org/10.47065/josyc.v6i1.6114.
A. N. Fadllurrohman, M. Lutfi, Q. Akbar, and A. Fathurohman, “Pemanfaatan Convolutional Neural Network untuk Klasifikasi Efisien dan Akurat pada Berbagai Jenis Sampah,” J. Komput. dan Teknol. Inf., vol. 2, no. 2, pp. 80–83, 2024.
I. Idawati, D. P. Rini, A. Primanita, and T. Saputra, “Klasifikasi Kanker Payudara Menggunakan Metode Convolutional Neural Network (CNN) dengan Arsitektur VGG-16,” J. Sist. Komput. dan Inform., vol. 5, no. 3, p. 529, 2024, doi: 10.30865/json.v5i3.7553.
A. M. K. Putri and A. F. Rozi, “Implementasi Convolutional Neural Network dalam Menentukan Tingkat Kematangan Mentimun dan Tomat Berdasarkan Warna Kulit,” JATI J. Mhs. Tek. Inform., vol. 8, no. 5, pp. 10388–10394, 2024.
M. A. Khair, P. Aldiyuda, and N. E. Pakpahan, “Perancangan Sistem Absensi Mahasiswa Berbasis Face Recognition di Lingkungan UPN Veteran Jakarta,” J. Inform., vol. 20, no. 1, pp. 35–42, 2024.
A. A. Handoko, M. A. Rosid, and U. Indahyanti, “Implementasi Convolutional Neural Network (CNN) Untuk Pengenalan Tulisan Tangan Aksara Bima,” SMATIKA STIKI Inform. J., vol. 14, no. 1, pp. 96–110, 2024, doi: https://doi.org/10.32664/smatika.v14i01.1196.
P. A. Maharani and M. Akbar, “Implementasi Convolutional Neural Network dalam Klasifikasi Jenis Kopi Temanggung,” JATI J. Mhs. Tek. Inform., vol. 8, no. 3, pp. 3030–3037, 2024.
L. Larwuy, “Optimasi Parameter Artificial Neural Network (ANN) Menggunakan Particle Swarm Optimization (PSO) Untuk Pengkategorian Nasabah Bank,” J. Mat. Komputasi dan Stat., vol. 3, no. 3, pp. 456–461, 2023.
B. F. G. Saputra, P. G. Chamdareno, and E. Dermawan, “Analisa Perbandingan Aliran Daya Optimal pada Sistem Standar IEEE 30 Bus Menggunakan Metode Particle Swarm Optimization (PSO) dan Gray Wolf Optimizer (GWO),” Resist. Elektron. Kendali Telekomun. Tenaga List. Komput., vol. 7, no. 1, 2024, doi: https://doi.org/10.24853/resistor.7.1.59-64.
A. M. Rizki and A. L. Nurlaili, “Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan,” J. Comput. Electron. Telecommun., vol. 1, no. 2, pp. 1–9, 2021, doi: https://doi.org/10.52435/complete.v1i2.73.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Deteksi Dini Kanker Payudara Menggunakan Citra Ultrasonografi Berbasis Convolutional Neural Networks dan Particle Swarm Optimization
Pages: 456-465
Copyright (c) 2025 Serina Azahra, Pramesti Kusumaningtyas, Mohammad Rofi'i
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
