Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches
Osteosarcoma is an aggressive and highly malignant bone cancer primarily affecting adolescents and young adults, with males being more commonly affected. Although deep learning models such as YOLO (95.73% accuracy) and VGG19 (95.25% accuracy), have demonstrated effectiveness in osteosarcoma detectio...
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| Format: | Article |
| Language: | English |
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State Islamic University Sunan Kalijaga
2025-02-01
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| Series: | IJID (International Journal on Informatics for Development) |
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| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4890 |
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| author | Muhammad Ainul Fikri Ajie Kusuma Wardhana Yudha Riwanto Inggrid Yanuar Risca Partiwi Fauzia Sekar Anis Sekar Ningrum Iqbal Kurniawan Asmar Putra |
| author_facet | Muhammad Ainul Fikri Ajie Kusuma Wardhana Yudha Riwanto Inggrid Yanuar Risca Partiwi Fauzia Sekar Anis Sekar Ningrum Iqbal Kurniawan Asmar Putra |
| author_sort | Muhammad Ainul Fikri |
| collection | DOAJ |
| description | Osteosarcoma is an aggressive and highly malignant bone cancer primarily affecting adolescents and young adults, with males being more commonly affected. Although deep learning models such as YOLO (95.73% accuracy) and VGG19 (95.25% accuracy), have demonstrated effectiveness in osteosarcoma detection, their large model sizes and extensive computational requirements limit their feasibility in resource-constrained environments. This study proposes a lightweight AI approach that optimizes osteosarcoma detection while maintaining high diagnostic accuracy, leveraging machine learning models under 5MB, manually or semi-automatically extracted features, and SMOTE for data balancing. Experimental results show that Random Forest, SVM, and XGBoost achieve accuracies of 94.70%, 94.23%, and 94.39%, respectively, closely matching the performance of YOLO and VGG19 while maintaining computational efficiency. Furthermore, the inference time for SVM is under one second (0.97s), demonstrating the speed advantage of lightweight models. These findings highlight the potential of small-size (lightweight) machine learning models to deliver high diagnostic accuracy with minimal computational requirements, providing a scalable and practical solution for early osteosarcoma detection in resource-limited settings. By balancing simplicity, efficiency, and high performance, this study establishes a new benchmark for achieving state-of-the-art results with lightweight models and paving the way for improved healthcare accessibility in underserved regions. |
| format | Article |
| id | doaj-art-c92d372e04774d2f926152bf7516993c |
| institution | DOAJ |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-c92d372e04774d2f926152bf7516993c2025-08-20T03:18:05ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482025-02-0113251752910.14421/ijid.2024.48904515Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning ApproachesMuhammad Ainul FikriAjie Kusuma WardhanaYudha RiwantoInggrid Yanuar Risca PartiwiFauzia Sekar Anis Sekar NingrumIqbal Kurniawan Asmar PutraOsteosarcoma is an aggressive and highly malignant bone cancer primarily affecting adolescents and young adults, with males being more commonly affected. Although deep learning models such as YOLO (95.73% accuracy) and VGG19 (95.25% accuracy), have demonstrated effectiveness in osteosarcoma detection, their large model sizes and extensive computational requirements limit their feasibility in resource-constrained environments. This study proposes a lightweight AI approach that optimizes osteosarcoma detection while maintaining high diagnostic accuracy, leveraging machine learning models under 5MB, manually or semi-automatically extracted features, and SMOTE for data balancing. Experimental results show that Random Forest, SVM, and XGBoost achieve accuracies of 94.70%, 94.23%, and 94.39%, respectively, closely matching the performance of YOLO and VGG19 while maintaining computational efficiency. Furthermore, the inference time for SVM is under one second (0.97s), demonstrating the speed advantage of lightweight models. These findings highlight the potential of small-size (lightweight) machine learning models to deliver high diagnostic accuracy with minimal computational requirements, providing a scalable and practical solution for early osteosarcoma detection in resource-limited settings. By balancing simplicity, efficiency, and high performance, this study establishes a new benchmark for achieving state-of-the-art results with lightweight models and paving the way for improved healthcare accessibility in underserved regions.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4890lightweight machine learningmedical diagnostic osteosarcoma detectionrandom forestsmote |
| spellingShingle | Muhammad Ainul Fikri Ajie Kusuma Wardhana Yudha Riwanto Inggrid Yanuar Risca Partiwi Fauzia Sekar Anis Sekar Ningrum Iqbal Kurniawan Asmar Putra Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches IJID (International Journal on Informatics for Development) lightweight machine learning medical diagnostic osteosarcoma detection random forest smote |
| title | Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches |
| title_full | Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches |
| title_fullStr | Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches |
| title_full_unstemmed | Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches |
| title_short | Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches |
| title_sort | improving osteosarcoma detection through smote driven machine learning approaches |
| topic | lightweight machine learning medical diagnostic osteosarcoma detection random forest smote |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4890 |
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