Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches

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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Bibliographic Details
Main Authors: Muhammad Ainul Fikri, Ajie Kusuma Wardhana, Yudha Riwanto, Inggrid Yanuar Risca Partiwi, Fauzia Sekar Anis Sekar Ningrum, Iqbal Kurniawan Asmar Putra
Format: Article
Language:English
Published: State Islamic University Sunan Kalijaga 2025-02-01
Series:IJID (International Journal on Informatics for Development)
Subjects:
lightweight machine learning
medical diagnostic
osteosarcoma detection
random forest
smote
Online Access:https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/4890
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