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All Journal Sainteks Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) PROSIDING SENATEK FAKULTAS TEKNIK UMP JRST (Jurnal Riset Sains dan Teknologi) Communications in Science and Technology JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Riset Rekayasa Elektro Proceedings Series on Physical & Formal Sciences Techno Jurnal Pengabdian Teknik dan Sains (JPTS)
Dian Nova Kusuma Hardani
Universitas Muhammadiyah Purwokerto
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Journal : Communications in Science and Technology

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A systematic review of breast cancer detection on thermal images Aqil Aqthobirrobbany; Dian Nova Kusuma Hardani; Indah Soesanti; Adi Nugroho, Hanung
Communications in Science and Technology Vol 8 No 2 (2023)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.8.2.2023.1270

Abstract

Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.
Decoding brain tumor insights: Evaluating CAM variants with 3D U-Net for segmentation Hardani, Dian Nova Kusuma; Ardiyanto, Igi; Adi Nugroho, Hanung
Communications in Science and Technology Vol 9 No 2 (2024)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.9.2.2024.1477

Abstract

Brain tumor segmentation is critical for effective diagnosis and treatment planning. While, conventional manual segmentation techniques are seen inefficient and variable, highlighting the need for automated methods. This study enhances medical image analysis, particularly in brain tumor segmentation by improving the explainability and accuracy of deep learning models, which are essential for clinical trust. Using the 3D U-Net architecture with the BraTS 2020 dataset, the study achieved precise localization and detailed segmentation with the mean recall values of 0.8939 for Whole Tumor (WT), 0.7941 for Enhancing Tumor (ET), and 0.7846 for Tumor Core (TC). The Dice coefficients were 0.9065 for WT, 0.8180 for TC, and 0.7715 for ET. By integrating explainable AI techniques, such as Class Activation Mapping (CAM) and its variants (Grad-CAM, Grad-CAM++, and Score-CAM), the study ensures high segmentation accuracy and transparency. Grad-CAM, in this case, provided the most reliable and detailed visual explanations, significantly enhancing model interpretability for clinical applications. This approach not only enhances the accuracy of brain tumor segmentation but also builds clinical trust by making model decisions more transparent and understandable. Finally, the combination of 3D U-Net and XAI techniques supports more effective diagnosis, treatment planning, and patient care in brain tumor management.
Co-Authors Abi Aditya Triyanda Agus Wardoyo Anton Yudhana Aqil Aqthobirrobbany Arif Johar Taufiq Burhanul Aziz Fajar Muhammad Fauzi Hanung Adi Nugroho Igi Ardiyanto Indah Soesanti Itmi Hidayat Kurniawan Itmi Hidayat Kurniawan Latiful Hayat Latiful Hayat Latiful Hayat M.Taufiq Tamam Muhammad Taufiq Tamam Nugroho Giri Jaladri Ruly Erwin AfanDika Setyo Adi Nugroho Sigit Rizky Pratama Son Ali Akbar Thomi Luthfianto Winarso dsl Winarso Winarso