Analisis Performa Deteksi Penyakit Paru-Paru dengan Model Klasifikasi Gambar Menggunakan LSTM Deep Learning

Anwar, Khoirul
Maruf, Rohim
Susanto, Fredy
Ryando, M. Bucci

Jurnal Ilmiah Universitas Batanghari Jambi
Vol 25, No 1 (2025): Februari

This research aims to analyze the performance of image classification models for lung disease detection using the long short-term memory (LSTM) deep learning method, and compare it with other methods such as convolutional neural networks (CNN). LSTM, which is commonly used in sequential data processing, is explored for its capabilities in handling medical imaging data. Performance comparisons are based on accuracy, precision, recall, and F1-score metrics, with data drawn from multiple sources of lung imaging datasets. The results of this study show that the LSTM method has certain advantages and disadvantages compared with CNN in terms of efficiency, detection accuracy, and generalization ability.