Klasifikasi Penyakit Pneumonia Menggunakan Metode Convolutional Neural Network Dengan Optimasi Adaptive Momentum

Abstract

Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.

Classification of x-ray images for detection of childhood pneumonia using pre-trained neural networks

“…The use of pre-trained neural networks with learning transfer has been shown to be efficient in classification, as discussed in this work, the state of the art is optimistic about the use of this technique. In the literature, applications for classification of pneumonia are presented that use approaches such as network construction, as is the case of the works Saraiva et al (2019), Andika et al (2019) and transfer of learning as an extractor of features such as the work of Toğaçar et al (2020) that uses a combination of three pre-trunked networks, alexnet, VGG16 and VGG19 to make the classification. The main contribution of this work is in the detailed analysis of performance of the Xception and NasNetLarge networks in the classification of three classes, separated in pairs, classification of viral x bacterial pneumonia and classification between pneumonia and normal.…”

Identification of Bacterial Spot Diseases on Paprika Leaves Using CNN and Transfer Learning

“…In conducting the evaluation, the researcher used the available test data plus the paprika leaf image data taken from google image to determine the performance of the model that had been made. Then to measure the performance of the model, several calculations will be used, including (Andika et al, 2019): 1. Accuracy is the calculation of the correct number of proportions from the classification results of the model 2.…”

Deteksi Pelat Nomor Dengan Menggunakan Optical Character Recognition Berbasis Algoritma LSTM

“…Pemisahan ini memungkinkan model untuk dievaluasi pada data yang belum pernah dilihat sebelumnya, memastikan kemampuan generalisasi dan mengidentifikasi potensi overfitting. Overfitting merupakan isu di mana model klasifikasi terlalu cocok dengan data pelatihan, yang dapat merugikan kinerja model pada data uji karena tidak semua informasi dalam data pelatihan relevan atau bermanfaat [17].…”