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M Habibi
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Computer Science & IT Electrical & Electronics Engineering

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A Herman Bedi Agtriadi; M Habibi; Zakiyah Misfazilah
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2501

Abstract

Breast cancer is the most common cancer globally with a malignant category that poses a serious and frightening threat to women. According to data from Globocan. In Indonesia alone in 2022 the number of new cases of breast cancer reached 66,271 cases, thus contributing (30,1.6%) of the total cancer cases in Indonesia. Of the cases with more than 22 thousand deaths, breast cancer is the second most deadly cancer. 70% of breast cancer cases are detected already at an advanced stage, where this case can occur due to delays in medical personnel who have not been able to detect breast cancer manually. This requires technology to help doctors and radiologists to evaluate Magnetic Resonance Imaging (MRI) images automatically. One of the deep learning methods useful for MRI image analysis is Convolutional Neural Network (CNN) using VGG19 and AlexNet architecture which has been proven in the classification process. This study uses data from Kaggle with a total of 1400 data. Through the use of the Convolutional Neural Network method, this study obtained a fairly optimal accuracy on the VGG19 architecture of 99% and on the AlexNet Architecture of 97%.
Co-Authors Herman Bedi Agtriadi Zakiyah Misfazilah