KLASIFIKASI PNEUMONIA PADA CITRA X-RAY DADA MENGGUNAKAN EKSTRAKSI CIRI CONVOLUTIONAL NEURAL NETWORK DAN ALGORITMA K-NEAREST NEIGHBOR
DOI:
https://doi.org/10.26623/elektrika.v18i1.14211Keywords:
citra X-Ray dada, convolutional neural network, K-nearest neighbor, deteksi pneumonia., klasifikasi citra medisAbstract
Pneumonia is an acute lung infection that remains one of the leading causes of death worldwide, particularly in children under five years of age and the elderly. Conventional diagnosis through chest X-ray interpretation requires high radiological expertise and is susceptible to human error, especially in regions with limited medical resources. This study proposes a two-stage automatic pneumonia classification system using Convolutional Neural Network (CNN) as an automatic feature extractor and K-Nearest Neighbor (KNN) as the final classifier on chest X-ray images. The dataset used is the publicly available Chest X-Ray Images (Pneumonia) from Kaggle, consisting of 5,863 images classified into two classes: Normal (1,583 images) and Pneumonia (4,280 images). The preprocessing pipeline includes grayscale conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), resizing to 224×224 pixels, and normalization to (0,1). A CNN architecture with three convolutional blocks (32–64–128 filters) with Batch Normalization extracts a 128-dimensional feature vector per image. The optimal K value for KNN is determined through validation experiments yielding K=5 with weighted Euclidean distance. The proposed system achieves an accuracy of 92.80%, precision of 94.12%, recall of 95.34%, F1-Score of 94.73%, specificity of 91.98%, and AUC-ROC of 0.971 on the test set, outperforming conventional methods including SVM+HOG (78.30%) and CNN end-to-end (90.45%).
Keywords: chest X-ray, convolutional neural network, K-nearest neighbor, medical image classification, pneumonia detection.
ABSTRAK
Pneumonia merupakan infeksi akut pada paru-paru yang masih menjadi salah satu penyebab kematian tertinggi di dunia, terutama pada anak-anak di bawah usia lima tahun dan lansia. Diagnosis konvensional melalui interpretasi citra X-Ray dada memerlukan keahlian radiologis yang tinggi dan rentan terhadap kesalahan manusia, khususnya di daerah dengan keterbatasan sumber daya medis. Penelitian ini mengusulkan sistem klasifikasi pneumonia otomatis dua tahap menggunakan Convolutional Neural Network (CNN) sebagai ekstraktor ciri otomatis dan K-Nearest Neighbor (KNN) sebagai pengklasifikasi akhir pada citra X-Ray dada. Dataset yang digunakan adalah Chest X-Ray Images (Pneumonia) dari Kaggle yang terdiri dari 5.863 citra dengan dua kelas: Normal (1.583 citra) dan Pneumonia (4.280 citra). Pipeline preprocessing meliputi konversi grayscale, peningkatan kontras menggunakan Contrast Limited Adaptive Histogram Equalization (CLAHE), resize ke 224×224 piksel, dan normalisasi ke rentang (0,1). Arsitektur CNN dengan tiga blok konvolusi (32–64–128 filter) disertai Batch Normalization mengekstrak vektor fitur 128 dimensi per citra. Nilai K optimal untuk KNN ditentukan melalui eksperimen validasi menghasilkan K=5 dengan jarak Euclidean berbobot. Sistem yang diusulkan mencapai akurasi 92,80%, presisi 94,12%, recall 95,34%, F1-Score 94,73%, specificity 91,98%, dan AUC-ROC 0,971 pada data uji, mengungguli metode konvensional termasuk SVM+HOG (78,30%) dan CNN end-to-end (90,45%).
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