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All Journal Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA
Muhammad Irsyad
Universitas Islam Negeri Sultan Syarif Kasim, Pekanbaru
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Analisa Gambar X-Ray Mammography dengan Convolution Neural Network pada Deep Learning dengan Arsitektur Resnet Nur Islamiati Sanusi; Siti Ramadhani; Muhammad Irsyad
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6365

Abstract

Cancer is a disease that occurs when cells in the body undergo changes and grow uncontrollably. Breast cancer is one of the common types of cancer that affects women worldwide. Early detection of breast cancer is crucial to improve the survival rate. Mammography is a medical imaging method used for the early detection of breast cancer. In this context, deep learning technology and computerized classifiers, such as Convolutional Neural Network (CNN) with the Resnet model, have been used for the analysis and prediction of mammography images with promising results. Previous studies have shown high accuracy in classifying breast masses as benign or malignant using CNN and Resnet. Furthermore, CNN has also been employed for the classification of malignant and benign breast cancer, prediction of breast cancer risk, as well as detection and classification of breast masses with satisfactory accuracy rates. The use of deep learning in medical image analysis, including mammograms and X-ray images, has proven to be an effective tool in improving cancer diagnosis and treatment. The dataset used consisted of 322 images divided into 7 classes. After testing, an accuracy of 72% was achieved with a 90:10 ratio of test data to training data, along with the corresponding confusion matrix values. Therefore, it can be concluded that the Resnet method is capable of identifying breast cancer.
Analisa Gambar X-Ray Mammography dengan Convolution Neural Network pada Deep Learning dengan Arsitektur Resnet Nur Islamiati Sanusi; Siti Ramadhani; Muhammad Irsyad
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6365

Abstract

Cancer is a disease that occurs when cells in the body undergo changes and grow uncontrollably. Breast cancer is one of the common types of cancer that affects women worldwide. Early detection of breast cancer is crucial to improve the survival rate. Mammography is a medical imaging method used for the early detection of breast cancer. In this context, deep learning technology and computerized classifiers, such as Convolutional Neural Network (CNN) with the Resnet model, have been used for the analysis and prediction of mammography images with promising results. Previous studies have shown high accuracy in classifying breast masses as benign or malignant using CNN and Resnet. Furthermore, CNN has also been employed for the classification of malignant and benign breast cancer, prediction of breast cancer risk, as well as detection and classification of breast masses with satisfactory accuracy rates. The use of deep learning in medical image analysis, including mammograms and X-ray images, has proven to be an effective tool in improving cancer diagnosis and treatment. The dataset used consisted of 322 images divided into 7 classes. After testing, an accuracy of 72% was achieved with a 90:10 ratio of test data to training data, along with the corresponding confusion matrix values. Therefore, it can be concluded that the Resnet method is capable of identifying breast cancer.
Klasifikasi Multikelas Citra Chest X-Ray Menggunakan Semi-Supervised SoftMatch pada Label Terbatas M. Nabil Dawami; Benny Sukma Negara; Muhammad Irsyad; Yusra Yusra; Febi Yanto
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9848

Abstract

Deep learning-based chest X-ray (CXR) classification frequently encounters bottlenecks due to the scarcity of labeled medical data and imbalanced class distributions. This study aims to implement a semi-supervised learning (SSL) approach utilizing the SoftMatch algorithm with a DenseNet-121 backbone for the multiclass classification of CXR images (Normal, COVID-19, and Pneumonia) under limited label conditions. SoftMatch is specifically selected for its capability to mitigate the quantity-quality trade-off through an adaptive pseudo-label soft-weighting mechanism. A dataset comprising 5,228 images is allocated via a stratified split into 70% training data, 10% validation data, and 20% testing data. Experiments are conducted across three labeled data proportion scenarios (5%, 10%, and 20%), each evaluated with and without Uniform Alignment. Evaluation metrics include accuracy, macro F1-score, confusion matrix, ROC-AUC, supported by visual interpretability analysis using Grad-CAM. The experimental results demonstrate that the model remains robust under the most critical scenario (5% labels), achieving an accuracy of 91.68% and a macro F1-score of 91.72% when integrating Uniform Alignment (UA), outperforming the scenario without UA, which records an accuracy of 90.73% and a macro F1-score of 90.82%. The best performance for the UA configuration is achieved in the 10% label scenario (accuracy 94.46%; macro F1-score 94.58%), while the peak overall performance is attained by the 20% label scenario without UA (accuracy 95.79%; macro F1-score 95.89%). These findings indicate that Uniform Alignment is effective in low-to-medium label conditions but does not consistently enhance performance at higher label proportions.
Co-Authors Benny Sukma Negara Febi Yanto M. Nabil Dawami Nur Islamiati Sanusi Siti Ramadhani Yusra Yusra