Klasifikasi Penyakit Paru-paru Berbasis Pengolahan Citra X Ray Menggunakan Convolutional Neural Network (classification Of The Lung Diseases Based On X Ray Image Processing Using Convolutional Neural Network)
DOI:
https://doi.org/10.34818/eoe.v9i2.17673Abstract
k Penyakit pada paru-paru merupakan gangguan yang cukup serius dimana dapat menyerang sistem pernapasan manusia dan bisa berakibat fatal jika tidak ditangani dengan serius. Pada saat ini deteksi penyakit pada paru-paru masih dilakukan secara manual oleh para dokter ahli, namun proses secara manual memakan waktu lama. Oleh karena itu, dalam tugas akhir ini dibuat sistem yang dapat mendeteksi dan mengklasifikasi penyakit paru-paru dengan otomatis.Pada Tugas Akhir ini merancang sistem otomatis untuk mengklasifikasi kondisi paru-paru berdasarkan citra x-ray paru-paru berbasis Convolutional Neural Network (CNN) menggunakan arsitektur MobileNet. Perancangan pada sistem dibagi menjadi beberapa tahapan dimulai dari menginput data citra x-ray paru-paru, tahap selanjutnya preprocessing, pada penelitian ini menggunakan dua jenis preprocessing, yaitu CLAHE, dan Gaussian filter, lalu dari hasil preprocessing dilakukan tahap pelatihan dengan dua jenis optimizer yang berbeda, yaitu Stochastic Gradient Descent (SGD), dan Adaptive moment (Adam). Tahap terakhir mengkalisifikasikan data citra menjadi empat kelas, yaitu Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal dan Tuberculosis. Hasil akhir dari penelitian ini menunjukan optimizer terbaik yaitu Adam menggunakan preprocessing CLAHE pada epoch 50 dan menghasilkan nilai akurasi sebesar 94,687 dan loss sebesar 0,148. Selain itu juga diperoleh hasil dari performansi sistem berupa presisi 95%, recall 93%, dan F-1 score sebesar 94%. Kata Kunci : CNN, MobileNet, citra x-ray paru-paru, Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal, Tuberculosis. Abstract Diseases of the lungs are quite serious disorders which can attack the human respiratory system and can be fatal if not treated seriously. At this time the detection of disease in the lungs is still check manually by expert doctors, but manual process takes a long time. Therefore, in this final project, a system is made that can detect and classify lung diseases automatically. using MobileNet architecture. The design of the system is divided into several stages starting from inputting lung x-ray image data, the next stage is preprocessing, in this study using two types of preprocessing, namely CLAHE, and Gaussian filters, then from the results of preprocessing, the training phase is carried out with two types of optimizers that different, namely Stochastic Gradient Descent (SGD), and Adaptive moment (Adam). The last stage is to classify image data into four classes, namely Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal and Tuberculosis. The final result of this study shows that the best optimizer is Adam using CLAHE preprocessing on epoch 50 and produces an accuracy value of 94,687 and a loss of 0.148. In addition, the results of the system performance are 95% precision, 93% recall, and an F-1 score of 94%. Keywords: CNN, MobileNet, lung x-ray images, Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal, TuberculosisReferences
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