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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pilar Nusa Mandiri Jurnal Teknologi Terpadu Jurnal Sistem Komputer dan Informatika (JSON) Infotek : Jurnal Informatika dan Teknologi Jurnal Pendidikan dan Teknologi Indonesia Kresna: Jurnal Riset dan Pengabdian Masyarakat
Nugraha Abdullah, Indra
Universitas Budi Luhur
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Breast Cancer Classification in Ultrasound Images Using Convolutional Neural Network (CNN) with Watershed Transform Method Muhammad Iqbal Fadillah; Ahmad Faishal Syafii; Indra Nugraha Abdullah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11499

Abstract

Breast cancer is one of the deadliest diseases, especially for women. Early diagnosis of breast lesions and differentiation of malignant nodules from benign nodules and normal nodules are important for breast cancer prognosis. In diagnosing this disease, one radiological method, namely medical image analysis using ultrasonography, can be used to determine early diagnosis of breast cancer. Breast cancer ultrasound images have several characteristics, such as color, shape, size, and texture, which make segmentation difficult due to object accumulation. This study implements a Convolutional Neural Network classification algorithm and modified watershed segmentation to separate nodules or tumors in breast cancer. From the segmentation performance test with Watershed Transform, the average ZSI index was 40% for malignant images and 60% for benign images. The results of the VGG architecture for classification modeling showed 47% for watershed segmentation and 80% without watershed segmentation.
Co-Authors -, nendi - Ahmad Faishal Syafii Amandus Jong Tallo Erdiansyah, Dian Hanifudin, Rizky Januar Insani, Oki Kusumaningsih, Dewi Kusumawardhany, Nidya Mohammad Syafrullah Muhammad Iqbal Fadillah Oki Januar Insani Purwanto Purwanto Ratna Kusumawardani Ratna Kusumawardani, Ratna Rosmani, Didi Ruwirohi, Jan Everhard Samidi Samidi Triana Anggraini Tutik Sri Susilowati