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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications Jurnal Mandiri IT
Muhammad Naufal Rachmatullah
Universitas Sriwijaya
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
Deep ensemble learning for skin lesions classification with convolutional neural network Renny Amalia Pratiwi; Siti Nurmaini; Dian Palupi Rini; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp563-570

Abstract

One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.
Multi-scale input reconstruction network and one-stage instance segmentation for enhancing heart defect prediction rate Sutarno, Sutarno; Nurmaini, Siti; Sapitri, Ade Iriani; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Darmawahyuni, Annisa; Firdaus, Firdaus; Islami, Anggun; Samsuryadi, Samsuryadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3404-3413

Abstract

Artifacts and unpredictable fetal movements can hinder clear fetal heart imaging during ultrasound scans, complicating anatomical identification. This study presents a new medical imaging approach that combines one-stage instance segmentation with ultrasound (US) video enhancement for precise fetal heart defect detection. This innovation allows real-time identification and timely medical intervention. The study acquired 100 fetal heart US videos from an Indonesian Hospital featuring cardiac septal defects, generating 1,000 frames for training, validation, and testing. Utilizing a combination of the multi-scale input reconstruction network (MIRNet) for image enhancement and YOLOv8l-seg for real-time instance segmentation, the method achieved outstanding validation results, boasting a 99.50% mAP for bounding box prediction and 98.40% for mask prediction. It delivered a remarkable real-time processing speed of 68.4 frames per second. In application to new patients, the method yielded a 65.93% mAP for bounding box prediction and 57.66% for mask prediction. This proposed approach offers a promising solution to early fetal heart defect detection using ultrasound, holding substantial potential for enhancing healthcare outcomes.
Co-Authors Abdurahman Ade Iriani Sapitri Ade Iriani Sapitri Ahmad Rifai Ahmad Rizky Fauzan Akhiar Wista Arum Akhtiar W Arum Al Farissi Al-Filambany, Muhammad Gibran Ananda, Dea Agustria Andre Herviant Juliano Anggun Islami Anggun Islami Anita Desiani Annisa Darmawahyuni Annisa Darmawahyuni Armansyah, Risky Arnaldo, Muhammad Arum, Akhiar Wista Bambang Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Darmawahyuni, Annisa Darmawahyuni, Annisa Desty Rodiah Dewi Chayanti Dian Palupi Rini Dian Palupi Rini Dinda Lestarini Dite Geovanni Erwin Erwin Erwin, Erwin Fadel Muhammad, Fadel Fahreza, Irvan Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Geovanni, Dite Hadipurnawan Satria Hanif Habibie Supriansyah Irvan Fahreza Islami, Anggun Kurniawan, Anggy Tias M. Fachrurrozi . Maharani, Masayu Nadila Masayu Nadila Maharani Mira Afrina Muhammad Akmal Shidqi Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Gibran Al-Filambany Muhammad Irham Rizki Fauzi Muhammad Taufik Roseno, Muhammad Taufik Novi Yusliani Patiyus Agustiansyah PATIYUS AGUSTIANSYAH, PATIYUS Putri Mirani Rahmat Fadli Isnanto Raihan Mufid Setiadi Raihan Mufid Setiadi Renny Amalia Pratiwi Reza Firsandaya Malik Ricy Firnando Ricy Firnando Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Ririn Purnama Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Setiadi, Raihan Mufid Shidqi, Muhammad Akmal Siti Nurmaini Sri Indra Maiyanti Sri Indra Maiyanti Suci Dwi Lestari Suci Dwi Lestari Sugandi Yahdin Sukemi Sukemi Sukemi Sutarno Sutarno Sutarno Syaputra, Hadi Tio Artha Nugraha Varindo Ockta Keneddi Putra Yesi Novaria Kunang