Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia)

Widiarto, Surya Adi; Saputra, Wahyu Andi; Dewi, Atika Ratna

doi:10.29100/jipi.v6i2.2102

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Penyebab citra x-ray yang buruk adalah peralatan yang kurang mumpuni, kesalahan operator, dan kelainan pasien. Oleh karena itu, perlu perbaikan kualitas citra sebelum diterapkan pada CNN (Widiarto et al, 2021 Histogram Equalization merupakan salah satu teknik dalam pemrosesan histogram citra. Teknik ini mengubah penyebaran nilai intensitas pixel dalam suatu citra menjadi merata (Kusuma, 2020).…”

Section: Pendahuluanunclassified

Implementasi HE, AHE, dan CLAHE Pada Metode Convolutional Neural Network untuk Identifikasi Citra X-Ray Paru-Paru Normal atau Terinfeksi Covid19

Hilmi,

Saputra

2023

Edukomputika

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Pada tanggal 7 Juni 2022 secara global terdapat 232 negara dengan total kasus infeksi Covid 19 mencapai 529.410.287 kasus serta 6.296.771 kasus kematian. Diagnosis secara cepat dan tepat diperlukan untuk menangani permasalahan tersebut, sehingga dapat menekan penyebaran virus yang semakin meluas dan tidak terkendali. Saat ini, diagnosis berbasis komputer dapat digunakan oleh tenaga medis dalam mendiagnosis pneumonia pada Covid 19. Diagnosis berbasis komputer dapat menggunakan teknologi klasifikasi dalam deep learning dengan memanfaatkan data citra x-ray. Salah satu metode dalam deep learning yang dapat menangani persoalan klasifikasi dan segmentasi melalui data citra yaitu Convolutional Neural Network (CNN). Penelitian ini melakukan pengujian perbandingan performa HE, AHE, dan CLAHE terhadap akurasi CNN yang diperoleh. Hasilnya akan digunakan sebagai model untuk mendiagnosis citra x-ray apakah citra termasuk kategori Covid 19 atau normal menggunakan metode transfer learning. Berdasarkan pengujian yang telah dilakukan performa model terbaik diperoleh model CLAHE 50 epochs dengan accuracy, precision, recall, f1-score, dan AUC 96.4 %. Sedangkan pengujian kemampuan model dalam mendiagnosis 40 citra baru diperoleh akurasi 100% untuk kelas Covid 19 dan 70% pada kelas normal dengan akurasi rata-rata 85%.

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Section: Pendahuluanunclassified

Implementasi HE, AHE, dan CLAHE Pada Metode Convolutional Neural Network untuk Identifikasi Citra X-Ray Paru-Paru Normal atau Terinfeksi Covid19

Hilmi,

Saputra

2023

Edukomputika

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“…Dataset Composition In general, the CNN architectural configuration is adapted to the CNN research configuration that has been carried out by[21]. In this study, a classification model was developed for cases of pneumonia associated with COVID-19.…”

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Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Saputra¹

2022

bits

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Pneumonia is a disease with symptoms of difficulty breathing, fever, dry cough, and chest pain. As an indication that a person has COVID-19, it is necessary to immediately identify pneumonia by a doctor whether the lung X-ray image of the patient is classified as pneumonia or not. The importance of early diagnosis can reduce the risk of death in patients. Convolutional Neural Network is one of the fields of study in the realm of Computer Science that can perform image-based object classification. CNN can be used to identify pneumonia based on thorax images based on the features or features displayed on the image. One of the important elements in CNN is the amount of data that can be used for training, validation, and testing. Generally, the more data entered, the more learning material for the CNN system so that the system can classify more accurately. This study aims to measure the accuracy of the CNN model on thoracic-pneumonia images with spatial level augmentation changes. Image augmentation is implemented to increase image variance with initial data of 5856 images. The applied augmentations are Affine, Flip, Pixel Dropout, Random Size Crop, and Shift Scale Rotate. The stages of this research are manually grouping images, implementing augmentation on images, applying training-validation-testing on CNN, and analyzing the output results of the developed system. By using 5 types of augmentation, the dataset used as learning material can be increased up to 5x the original amount. From the research carried out, it was found that the Random-sized Crop type augmentation gave the highest accuracy value of 94.719% or an increase of 3.808% from the non-augmentation testing data. From this research, it is hoped that studies related to augmentation can be a reference regarding the type of augmentation process and its results in finding the CNN accuracy value, especially in the case of pneumonia classification

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Fourier Domain Adaptation for Image Augmentation in CNN-based Pneumonia Classification

Saputra,

Muhammad,

Septiani

2023

2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)

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Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia)

Cited by 3 publications

References 11 publications

Implementasi HE, AHE, dan CLAHE Pada Metode Convolutional Neural Network untuk Identifikasi Citra X-Ray Paru-Paru Normal atau Terinfeksi Covid19

Implementasi HE, AHE, dan CLAHE Pada Metode Convolutional Neural Network untuk Identifikasi Citra X-Ray Paru-Paru Normal atau Terinfeksi Covid19

Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Fourier Domain Adaptation for Image Augmentation in CNN-based Pneumonia Classification

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