What is Image Data Classification?
Image data classification is a technique that allows researchers to group, compare, and explore patterns within large sets of biological images. This tool supports key steps such as grayscale conversion, dimensionality reduction via PCA and t-SNE, and clustering using k-means or hierarchical clustering (HCD).
With this tool, users can upload up to 50 images, analyze them visually, and identify hidden structures in their data—without needing to install any software. Each image is automatically processed to ensure proper size and format, and interactive plots are generated to show cluster separation and sample relationships.
BioChemCalc’s classification tool is free, web-based, and easy to use, making it ideal for both researchers and educators working in biology, medicine, or data science.
Traditionally, researchers relied on manual classification of histological or cellular images based on visual observation. This tool allows for a shift to statistically driven image analysis using PCA and t-SNE, reducing subjective bias and enhancing reproducibility.
It is particularly useful for classifying image sets such as wild-type vs mutant vs treated conditions, and can reveal subtle internal variations or progression patterns that may be difficult to detect visually. Time-course changes can also be tracked quantitatively through cluster shifts.
In addition to visualizing data structure, this tool can uncover outlier images, assist in quality control, and serve as an educational aid in teaching biological data interpretation with machine learning concepts.
