Classification of Chest Disease Detection Using X-Ray Images through the Implantation of Efficientnetv2

Abstract—Chest diseases encompass a broad spectrum of conditions that can severely affect both respiratory and cardiovascular health. These diseases may arise from various factors, including infections, environmental influences, genetic predispositions, and lifestyle choices. To address these challenges, this project proposes a system for chest disease detection using X-ray images, employing the EfficientNet V2 model.The methodology begins with preprocessing steps, where an Adaptive Median Filter is used to enhance the quality of X-ray images by reducing noise while preserving important features for analysis. After preprocessing, Fuzzy C-Means (FCM) Image Segmentation is applied to precisely identify key regions within the images, improving the robustness of the classification model. EfficientNet V2 is then employed to extract features and classify the images, offering improved accuracy compared to traditional methods.Additionally, Local Binary Patterns (LBP) are incorporated for feature extraction, further enriching the input data for the model.This proposed framework aims to automate the diagnostic process, reducing the reliance on manual interpretation by radiologists. Experimental results demonstrate the effectiveness of this approach in accurately identifying and classifying chest diseases, thereby contributing to faster and more efficient patient care. The entire project is implemented using Python. Keywords—Fuzzy C-Means (FCM),Local Binary Patterns (LBP), Convolutional Neural Network, Lung Disease.