Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN)


  • Anita Loi Universitas Prima Indonesia, Medan, Indonesia
  • Ruth N Panjaitan Universitas Prima Indonesia, Medan, Indonesia
  • Saut Dohot Siregar Universitas Prima Indonesia, Medan, Indonesia
  • Allwin M Simarmata Universitas Prima Indonesia, Medan, Indonesia




Breast Cancer, CT Scan, Classification, Convolutional Neural Network


A common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support.

GS Cited Analysis


Download data is not yet available.


M. D. Hapsari and E. I. Setyawan, Potensi Antikanker Ekstrak Buah Jamblang (Syzygium cumini L.) sebagai Bahan Pangan Fungsional, Vol. 2, PP. 356–368, 2023.

Z. Momenimovahed and H. Salehiniya, Epidemiological characteristics of and risk factors for breast cancer in the world, Breast Cancer Targets Ther., Vol. 11, PP. 151–164, 2019, doi: 10.2147/BCTT.S176070.

M. Pegram, C. Jackisch, and S. R. D. Johnston, Estrogen/HER2 receptor crosstalk in breast cancer: combination therapies to improve outcomes for patients with hormone receptor-positive/HER2-positive breast cancer, npj Breast Cancer, Vol. 9, no. 1, 2023, doi: 10.1038/s41523-023-00533-2.

Pemprov, Data Kanker Payudara 2022 Sumut, 2023.

S. L. Anwar et al., Risk factors of distant metastasis after surgery among different breast cancer subtypes: A hospital-based study in Indonesia, World J. Surg. Oncol., Vol. 18, no. 1, PP. 1–16, 2020, doi: 10.1186/s12957-020-01893-w.

C. B. S. Maior, J. M. M. Santana, I. D. Lins, and M. J. C. Moura, Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases, PLoS One, Vol. 16, No. 3 March, 2021, doi: 10.1371/journal.pone.0247839.

D. Singh, V. Kumar, Vaishali, and M. Kaur, Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks, Eur. J. Clin. Microbiol. Infect. Dis., Vol. 39, no. 7, PP. 1379–1389, 2020, doi: 10.1007/s10096-020-03901-z..

S. Serte and H. Demirel, “Deep learning for diagnosis of COVID-19 using 3D CT scans,” Comput. Biol. Med., Vol. 132, No. October 2020, PP. 104306, 2021, doi: 10.1016/j.compbiomed.2021.104306..

M. Sahu and R. Dash, A survey on deep learning: Convolution neural network (CNN), Vol. 153, Springer Singapore, 2021. doi: 10.1007/978-981-15-6202-0_32.

M. W. Muhammad Afrizal Amrustian, “mplementasi Metode Convolutional Neural Network untuk Klasifikasi Breast Cancer pada Citra Histopatologi, Media Inform., Vol. 7, No. 2, PP. 10, 2023, doi: 10.30865/mib.v7i1.5194..

M. Harahap, S. K. Anjelli, W. A. M. Sinaga, R. Alward, J. F. W. Manawan, and A. M. Husein, Classification of diabetic foot ulcer using convolutional neural network (CNN) in diabetic patients, J. Infotel, Vol. 14, No. 3, PP. 196–202, 2022, doi: 10.20895/infotel.v14i3.796.

R. Rokhana et al., Convolutional Neural Network untuk Pendeteksian Patah Tulang Femur pada Citra Ultrasonik B–Mode, J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 8, No. 1, PP. 59, 2019, doi: 10.22146/jnteti.v8i1.491.

M. Lestandy, Deteksi Dini Kanker Payudara Menggunakan Metode Convolution Neural Network (CNN), Inspir. J. Teknol. Inf. dan Komun., Vol. 12, No. 1, PP. 65, 2022, doi: 10.35585/inspir.v12i1.2667.

F. A. A. Harahap, A. N. Nafisa, E. Purba, and ..., Implementasi Algoritma Convolutional Neural Network Arsitektur Model MobileNetV2 dalam Klasifikasi Penyakit Tumor Otak Glioma, Pituitary dan Meningioma, J. Teknol. …, Vol. 5, No. 1, PP. 53–61, 2023, [Online]. Available:

M. A. Hanin, R. Patmasari, and R. Y. Nur, Sistem Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network (CNN) Skin Disease Classification System Using Convolutional Neural Network (CNN), e-Proceeding Eng., Vol. 8, No. 1, PP. 273–281, 2021.

R. Andre, B. Wahyu, and R. Purbaningtyas, Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Dengan Arsitektur Efficientnet-B3, J. IT, Vol. 11, No. 3, PP. 55–59, 2021, [Online]. Available:

J. Kecerdasan, T. Informasi, D. H. Firdaus, B. Imran, L. D. Bakti, and E. Suryadi, Klasifikasi Penyakit Katarak Pada Mata Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Web Web-Based Classification of Cataract in the Eyes Using Convolutional Neural Network (CNN) Method, J. Kecerdasan Buatan dan Teknol. Inf., vol. 1, no. 3, pp. 18–26, 2022.


Crossmark Updates

How to Cite

Loi, A. ., Panjaitan, R. N., Siregar, S. D. ., & Simarmata, A. M. . (2024). Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN). Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1551-1557.