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All Journal Sriwijaya Bioscientia Jurnal Dehasen Mengabdi Jurnal Komputer
Sari, Nely Puspita
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Immunology & microbiology Medicine & Pharmacology Social Sciences

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Journal : Jurnal Komputer

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Analisis Performa Algoritma CNN dalam Klasifikasi Citra Medis Berbasis Deep Learning Sari, Nely Puspita
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.113

Abstract

The advancement of artificial intelligence technologies, particularly in the field of deep learning, has driven the application of Convolutional Neural Network (CNN) algorithms in various domains, including medical image classification. This study aims to analyze the performance of CNN in classifying medical images associated with different diseases using a standard CNN architecture. The dataset utilized consists of labeled X-ray and MRI images based on medical diagnoses. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess how effectively the model recognizes complex visual patterns. The results demonstrate that CNN achieves high accuracy in identifying objects within medical images, with an average F1-score exceeding 90% on selected datasets. These findings suggest that CNN has significant potential to support automated and efficient medical diagnosis, although further clinical validation is necessary for real-world implementation.
Co-Authors Ahmad Soleh Amelya, Tifani Rika Elfachmi, Elfachmi Fitriano, Yun Lutfiani, Irma Neri Susanti Panjaitan, Klara Mega Utari Sari, Meisie Novia