Implementasi Deteksi Tumor Otak Menggunakan YOLOv11 dan Flask
DOI:
https://doi.org/10.37859/jf.v15i2.9703
Abstract
Kecerdasan buatan (AI) telah mengalami kemajuan yang sangat signifikan untuk membantu kehidupan masyarakat salah satunya adalah bidang kesehatan. Kemajuan AI didorong karena banyaknya kesalahan yang diakibatkan beberapa faktor fundamental dan tingginya permintaan dari masyarakat terhadap layanan kesehatan terus meningkat. AI juga mampu meminimalkan kesalahan diagnosa maupun pengobatan dalam praktik klinis pasien seperti deteksi tumor otak. Algoritma YOLO yang sering digunakan untuk deteksi objek karena akurasi yang tinggi. YOLO juga dapat digunakan untuk real-time diagnosa menjadi nilai tambah pada algoritma tersebut. YOLOv11 merupakan algoritma terbaru dan memiliki performa yang lebih baik dibandingkan seri sebelumnya. Meskipun begitu, tantangan terhadap keterbatasan dataset menjadi salah satu permasalahan yang perlu diselesaikan. Penelitian yang dilakukan memiliki tujuan yaitu meningkatkan jumlah dataset citra medis menggunakan Data Augmentasi dan mengintegrasikan algoritma YOLO dengan Flask untuk memberikan tampilan yang lebih baik kepada pengguna. Penelitian yang dilakukan menggunakan Data Augmentasi pada dataset menggunakan teknik Flip (Horizontal dan Vertical), 90° Rotate (Clockwise, Counter-Clockwise, Upside Down), serta penambahan Noise: Up to 1.5% of pixels. Hasilnya, diperoleh F1-score 0.951 dari 4 kelas (0.902 Glioma, 0.989 Meningioma, 0.915 Pituitary, dan 0.997 No tumor). Sehingga terbukti efektif mengatasi keterbatasan data. Selanjutnya, Integrasi YOLO dengan Flask dapat memberikan tampilan deteksi objek yang lebih baik tanpa menurunkan skor dari hasil deteksi objek tumor otak, sehingga Flask dapat dijadikan framework yang dipertimbangkan untuk pengembangan interface machine learning
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