COMPARATIVE ANALYSIS OF LUNG DISEASES FROM CHEST X-RAY IMAGES USING CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE - Ningsiah, Ningsiah; Irianto, Suhendro Yusuf (Universitas Aisyah Pringsewu, 2024)
The lungs are an important human organ in the human body, especially in the respiratory system. Another function of the lungs is to maintain stable body temperature, protect the body from dangerous substances, the nose is the sense of smell, but sometimes the lungs will experience conditions where they do not function normally. Chest x-ray images are the most well-known clinical method for the diagnosis of lung diseases. However, diagnosing lung diseases from chest x-ray images is a challenging task even for radiologists. This research proposes a system that can be used for comparative analysis of lung disease by applying the Convolutional Neural Network and Support Vector Machine methods. CNN is a method in the field of object recognition that has special layers, namely convolution layers and pooling layers which enable a good feature learning process. SVM is a comparative analysis method that relies on results from statistical learning theory to guarantee generalization performance. In this research there are 2 main processes, namely preprocessing and comparative analysis. There are 3 classes of disease for comparative analysis, namely Covid-19 disease, Tuberculosis disease, Pneumonia disease, and Normal disease. In this study, a comparison was also carried out between the classification carried out by CNN and SVM. The research data uses a chest X-ray image dataset. This research produces the best algorithm that is implemented to classify lung diseases from chest x-ray images.