Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers

show abstract

“…Researchers also combine the saliency method to perform classification. The accuracy obtained was 95% (Yohannes, Devella, and Arianto 2020).…”

Section: Literature Reviewmentioning

confidence: 86%

“…CNN has been shown to classify red blood cell images with sufficient accuracy compared to other image classification methods (Liang et al 2017). In previous studies, the CNN architecture was used to classify red blood cells quite deeply (Yohannes, Devella, and Arianto 2020).…”

Section: Introductionmentioning

confidence: 99%

Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

Harahap

Jefferson

Barti

et al. 2020

SinkrOn

View full text Add to dashboard Cite

show abstract

“…Penelitian ini menggunakan model Convolutional Neural Network (CNN) sebagai arsitektur model klasifikasi citra multi-label pada motif batik. CNN termasuk salah satu algoritma deep learning yang merupakan pengembangan dari multi-layer perceptron (MLP), sehingga CNN dapat digunakan untuk melakukan klasifikasi data yang berlabel [13]. Namun, berbeda dari MLP yang merepresentasikan neuronnya dalam satu dimensi, CNN merepresentasikan neuronnya dalam bentuk dua dimensi [14].…”

Section: Geometrisunclassified

Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik

Bariyah

Rasyidi

Ngatini

2021

View full text Add to dashboard Cite

Salah satu warisan budaya Indonesia yang diakui dunia adalah kain batik. Beragamnya motif batik di Indonesia membuat masyarakat awam sulit membedakan motif-motif yang ada. Penelitian ini menggunakan convolutional neural network (CNN) dalam melakukan klasifikasi multi-label citra motif batik. CNN merupakan salah satu algoritma deep learning pengembangan multi-layer perceptron (MLP) yang telah banyak digunakan dalam klasifikasi data, khususnya klasifikasi citra. Hasil penelitian menunjukkan akurasi penggunaan arsitektur CNN dalam melakukan klasifikasi multi-label pada 15 motif batik mencapai 91.41% dengan penggunaan epoch 100.

show abstract

“…Saliency lebih berfokus pada area atau wilayah dan banyak digunakan dalam bidang computer vision, pengenalan objek, dll [6]. Saliency dapat digunakan untuk deteksi penyakit malaria menggunakan CNN [7]. Tidak hanya itu, saliency juga digunakan dalam kombinasi pembentukan fitur bentuk dan warna dalam klasifikasi.…”

Section: Pendahuluanunclassified

Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih

Yohannes

Devella

Hadisaputra

2021

JuTISI

Self Cite

View full text Add to dashboard Cite

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.

show abstract

Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency

Cited by 13 publications

References 0 publications

Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik

Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih

Contact Info

Product

Resources

About