Manna Omax: Pulmonary Disease Detection Using Texture Properties and Dissimilarity

This study evaluates the performance of traditional texture descriptors and dissimilarity-based methods for pulmonary disease classification. The dataset consists of 10,631 publicly available images categorized into four classes: pneumonia, tuberculosis, COVID-19, and healthy. Feature extraction was performed using Local Binary Patterns (LBP), and classification was conducted with K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Convolutions Neural Network (CNN) Resnet, Vgg16, EfficientNetBO, and dissimilarity-based approaches (spatial, vectorial, and embeddings). Additionally, a hierarchical process was applied to enhance traditional classification methods. The results indicate that spatial dissimilarity achieved the highest accuracy (0.977) and the most efective using only 500 images per class obtain a 0.972 accuracy, demonstrating its effectiveness in reducing data requirements while maintaining high performance.