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All Journal Journal of Information Technology and Cyber Security
Aziz, Umar Alfaruq Abdul
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Classification Techniques in Finding Malignant Breast Cancer Detection Whardana, Adithya Kusuma; Mufti, Abdul Latief; Hermawan, Hendar; Aziz, Umar Alfaruq Abdul
Journal of Information Technology and Cyber Security Vol. 2 No. 1 (2024): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.8829

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%.
Co-Authors Hermawan, Hendar Mufti, Abdul Latief Whardana, Adithya Kusuma