Classification Techniques in Finding Malignant Breast Cancer Detection

DOI: https://doi.org/10.30996/jitcs.8829
Keywords: Breast Cancer research, Classification, CNN, Convolutional Neural Network, Digital Image Processing, Image Analysis

Abstract

The most fundamental aspect of cancer is that it is marked by abnormal and uncontrolled cell growth, allowing it to spread to the surrounding areas of existing tissues. One of the most common cancers experienced by people in Indonesia, according to the Indonesian Ministry of Health, is breast cancer. The diagnosis of diseases, especially cancer, also requires a visual form that is later used as an image to determine the condition within the patient's organs. The use of mammography images is one implementation of X-rays aimed at revealing the structure of human bones and tissues. The use of images is also recognized in information technology in the field of digital image processing, which is useful for analyzing, enhancing, compressing, and reconstructing images using a collection of computational techniques. One application of digital image processing techniques for breast mammography images is recognizing the possibility of breast cancer through computer automation using classification methods supported by googlepredict.net architectures. The results obtained in this study use a dataset sourced from King Abdul Aziz University, totaling 2378 images. The method used in this research is Convolutional Neural Network (CNN), with the addition of the GoogleNet architecture. The convolution extraction method runs with the GoogleNet architecture, enhancing deep learning for optimal breast cancer recognition. The overall results of this study found an average precision value of 90%, recall of 92%, F-1 Score of 91.49%, and accuracy of 91.49%.

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Author Biographies

Adithya Kusuma Whardana, Universitas Tanri Abeng

Department of Informatics Engineering

Abdul Latief Mufti, Universitas Tanri Abeng

Department of Informatics Engineering

Hendar Hermawan, Universitas Tanri Abeng

Department of Informatics Engineering

Umar Alfaruq Abdul Aziz, Universitas Tanri Abeng

Department of Informatics Engineering

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Published
2024-07-30
How to Cite
Whardana, A. K., Mufti, A. L., Hermawan, H., & Aziz, U. A. A. (2024). Classification Techniques in Finding Malignant Breast Cancer Detection. Journal of Information Technology and Cyber Security, 2(1), 40-50. https://doi.org/10.30996/jitcs.8829
Issue
Vol 2 No 1 (2024): January
Section
Research Article

Copyright (c) 2024 Adithya Kusuma Whardana, Abdul Latief Mufti, Hendar Hermawan, Umar Alfaruq Abdul Aziz

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