Pneumonia Medical Image Classification Using Convolution Neural Network Model AlexNet& GoogleNet

Abstract

Pneumonia is one of the deadliest diseases in the world. Diagnosis of pneumonia is done with the help of CT-scan image analysis of the chest. This analysis is usually done by a pulmonary specialist. The availability of pulmonary specialists is still limited, especially in underdeveloped, outermost and frontier (3T) areas. In addition, manual analysis still faces the possibility of errors. The use of artificial intelligence technology is expected to overcome these problems. The purpose of this study is to obtain the results of pneumonia disease classification using the CNN algorithm using the AlexNet and GoogleNet models. The tools used in this research are python. The image dataset used amounted to 5856 images obtained from the Kaggle repository. The stages of this research consist of data preparation where this data has been preprocessed and split data. Furthermore, the CNN stage with the architecture used is AlexNet and GoogleNet. . The training data used is 90% of the data or 5270 images and the testing data is 10% or 586 images. Model training is carried out as many as 20 iterations so that the model used can recognize. The training model is done in as many as 20 iterations so that the model used can recognize the image more accurately. After the model has been trained the model will be tested by providing test data. The results of this research are displayed in the confusion matrix. The results of the research using the AlexNet and GoogleNet architectures get an accuracy value. This accuracy value is then compared between the two. The accuracy obtained from AlexNet architecture is 96% while that obtained from GoogleNet is 94%. From the results of the accuracy of the two models, it can be concluded that the AlexNet architecture has the highest accuracy of 96%.