Biomedical Images Dataset

This repository contains the data from the manuscript "MaZda versus PyRadiomics – Comparison of the Effectiveness of Texture Feature Estimation in the Context of Classifying Selected Biomedical Images". During data collection, repositories available on the Kaggle and ISIC platforms were used, as well as the authors’ own resources prepared for earlier research. Five datasets containing images recorded using various imaging techniques were prepared. The characteristics of these datasets, including their division into observation classes, were as follows:• chest X-ray images – 2000 images divided into 4 classes (Normal, Covid-19, Viral Pneumonia, Lung Opacity);• lung CT images – 1000 images divided into 2 classes (Non-Covid, Covid-19);• brain MRI images – 2000 images divided into 4 classes (Normal, Glioma Tumor, Meningioma Tumor, Pituitary Tumor);• thyroid ultrasound images – 800 images divided into 2 classes (Normal, Hashimoto’s Disease);• dermoscopic images of skin lesions – 1600 images divided into 4 classes (Nevus, Seborrheic Keratosis, Malignant Melanoma, Basal Cell Carcinoma).

As part of the image pre-processing, cropping and conversion to an 8-bit grayscale format were performed. All datasets were randomly divided into training and testing parts (70% and 30% of the full set respectively). In all datasets, the observation classes are balanced (each class contains the same number of samples). The collection of images contained in this repository can be used for testing and comparing methods of texture feature estimation, such as measures based on the gray-level co-occurrence matrix (GLCM), LBP descriptors, or wavelet transform parameters. In addition, they provide a suitable environment for validating image classification algorithms, both traditional ones (K-NN, SVM, decision trees) and those based on deep neural networks. Thanks to the controlled experimental conditions and known class labels, it is possible to objectively compare the effectiveness of individual approaches and optimize model architectures.

Using this dataset please cite:Omiotek, Z., Boyko, O. (2026). MaZda versus PyRadiomics – comparison of the effectiveness of texture feature estimation in the context of classifying selected biomedical images. Przegląd Elektrotechniczny, 102(5), 117-126. DOI: 10.15199/48.2026.05.15.