Bisakah AI Mendeteksi COVID-19 dari Chest X-Ray?

2026-02-13 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

A convolutional neural network (CNN) model was developed to detect COVID-19 from chest X-ray images, offering a faster alternative to PCR tests. The model processes image data through extraction layers, including convolutional, pooling, and ReLU units, followed by classification layers with fully connected, dropout, and softmax components. The dataset comprised 3616 COVID-19 and 10192 normal chest X-ray images, which underwent preprocessing steps like resizing to 64x64 pixels, labeling, splitting into 80:20 training/testing sets, and pixel rescaling. The CNN was trained for 40 epochs with a learning rate of 0.1, demonstrating decreasing loss and increasing accuracy. Evaluation using a confusion matrix yielded an accuracy of 91%, precision of 86%, and recall of 79%, indicating strong classification performance for COVID-19 detection.

Key takeaway

For AI Engineers developing medical diagnostic tools, this CNN approach for COVID-19 detection from chest X-rays demonstrates a viable, high-performing method. You should consider similar CNN architectures and data preprocessing steps, including image standardization and pixel rescaling, when building rapid diagnostic systems. The reported 91% accuracy, 86% precision, and 79% recall provide a strong benchmark for evaluating your own model's effectiveness in binary classification tasks.

Key insights

CNNs can effectively classify COVID-19 from chest X-rays, offering a rapid diagnostic aid.

Principles

• CNNs process images via extraction and classification layers.
• Data preprocessing is crucial for model performance.
• Performance metrics like accuracy, precision, and recall evaluate classification.

Method

The method involves standardizing X-ray images to 64x64 pixels, labeling, splitting data (80:20), rescaling pixel values, and training a CNN with backpropagation over 40 epochs using a 0.1 learning rate.

In practice

• Use 64x64 pixel standardization for medical images.
• Apply 80:20 train/test split for model validation.
• Evaluate classification with a confusion matrix.

Topics

• Convolutional Neural Networks
• COVID-19 Detection
• Chest X-ray Analysis
• Deep Learning
• Medical AI Diagnostics

Best for: AI Engineer, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.