Processed Prostate MRI Dataset for Early Cancer Detection using Machine Learning

This dataset supports the development of an automated prostate cancer detection model using machine learning. Pre-processing steps include: a) Resizing to 224×224 pixels b) Grayscale conversion c) Noise reduction using a median filter d) Contrast stretching for enhancement e) Segmentation using Otsu thresholding and ROI contour detection Feature extraction was performed using a Gray Level Co-Occurrence Matrix (GLCM) in four orientations (0°, 45°, 90°, and 135°), resulting in 16 statistical texture features (contrast, correlation, energy, and homogeneity). These features were normalized using Min-Max scaling. Dataset Composition: 1. Number of images: 961 a) 424 positive cases (Prostate cancer detected) b) 537 negative cases (Cancer not detected) 2. Tabular feature dataset (CSV) containing: a) 16 GLCM features b) Class labels (0 = Negative, 1 = Positive) File Structure: a) /images/positive/ → MRI scans with prostate cancer b) /images/negative/ → MRI scans without prostate cancer c) metadata.csv → Tabular feature dataset with extracted GLCM features and labels d) readme.txt → Detailed preprocessing and extraction steps Applications: a) Machine learning classification (XGBoost, Random Forest, CNN, etc.) b) Image analysis Medical c) Research on early diagnosis of prostate cancer