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INDONESIA
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568624     DOI : https://doi.org/10.35882/ijeeemi
Core Subject : Health, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) publishes peer-reviewed, original research and review articles in an open-access format. Accepted articles span the full extent of the Electronics, Biomedical, and Medical Informatics. IJEEEMI seeks to be the world’s premier open-access outlet for academic research. As such, unlike traditional journals, IJEEEMI does not limit content due to page budgets or thematic significance. Rather, IJEEEMI evaluates the scientific and research methods of each article for validity and accepts articles solely on the basis of the research. Likewise, by not restricting papers to a narrow discipline, IJEEEMI facilitates the discovery of the connections between papers, whether within or between disciplines. The scope of the IJEEEMI, covers: Electronics: Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, Electromedical: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design. Medical Informatics: Intelligent Biomedical Informatics, Computer-aided medical decision support systems using heuristic, Educational computer-based programs pertaining to medical informatics
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 21 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): May" : 21 Documents clear
Hybrid features to classify lung tumor using machine learning Rahmawan, Rizki Dwi; Salamah, Umi; Yudha, Ery Permana
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.101

Abstract

A lung tumor is an abnormal mass of cells inside a body. As a benign tumor is unproblematic, but a malignant tumor is cancerous because it can travel across the body and interfere with its surrounding tissue. Detecting these cancerous cells in the lung is important because delayed detection may hamper effective treatment options, leading to a lower survival rate. However, classifying tumor malignancy is highly dependent on the knowledge and experience of the radiologist. This study combines texture-based features extracted from lung Computed Tomography Scan (CT Scan) images such as Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GLRLM), Gray Level Size-zone Matrix (GLSZM), and Haralick Features aims to create a lung tumor classification system. This research contributes by creating an efficient and reliable system through Relief-F feature selection that uses features with the highest weight in rank that are able to differentiate classes of tumor malignancy and help medical professionals diagnose tumors more early in the treatment.  As a comparison, several conventional machine learning classifiers, including SVM RBF, KNN, RF, DT, and XGBoost, were utilized to evaluate classifier performance. The result showed that the accuracy of the proposed hybrid features with a random forest classifier was the most performing approach with an evaluation score of accuracy of 99.55%, precision of 99.55%, recall of 99.55%, and F1-Score of 99.54%. Furthermore, accuracy among other classifiers was also higher than 90%. Proofing the selected features retain essential class information, demonstrating the study’s applicability in developing automated lung tumor classification systems from CT scans.

Page 3 of 3 | Total Record : 21

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