Early Breast Cancer Detection Using Gabor Filter and Convolutional Neural Network for Microcalcification Identification

Authors

DOI:

https://doi.org/10.30996/jitcs.132037

Abstract

Breast cancer poses a considerable challenge in Indonesia, resulting in numerous fatalities. This study aims to improve the accuracy and efficiency of early breast cancer diagnosis by leveraging modern image processing and artificial intelligence. The dataset used is the Mini-DDSM (Mini Digital Database for Screening Mammography), taken from Kaggle and vetted by radiologists into a Region of Interest (ROI) consisting of three categories: Benign, Cancer, and Normal. The methodology encompasses comprehensive image preprocessing, which includes resizing, cropping, RGB-to-grayscale conversion, Laplacian of Gaussian (LoG) filtering, Gabor filtering, global threshold segmentation, and image enhancement. A Convolutional Neural Network (CNN) is employed for classification purposes. Ninety percent of the images are allocated for training, while 10% are designated for testing, with critical parameters such as learning rate, batch size, and epochs being tuned throughout the training process. The CNN architecture was assessed based on recognition rate, error rate, epoch count, and training duration. The results provide a flawless validation accuracy of 100% over 32 trials. The findings demonstrate that the suggested method markedly enhances early breast cancer identification using microcalcification analysis in mammography images, assisting medical professionals in early diagnosis and potentially elevating patient recovery rates through prompt detection and treatment.

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Author Biographies

Abdul Latief Mufti, Universitas Tanri Abeng

Department of Informatics Engineering

Adithya Kusuma Whardana, Universitas Tanri Abeng

Department of Informatics Engineering

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Published

2025-10-07

How to Cite

Mufti, A. L., & Whardana, A. K. (2025). Early Breast Cancer Detection Using Gabor Filter and Convolutional Neural Network for Microcalcification Identification. Journal of Information Technology and Cyber Security, 3(2), 128–146. https://doi.org/10.30996/jitcs.132037

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Vol. 3 No. 2 (2025): July

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Research Article

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