RADIOMICS BASED METHOD FOR PREDICTING THE ONSET OF HUMAN DISEASES USING NEURAL NETWORKS AND COLOR SPACE ANALYSIS

Abstract

The present invention provides a radiomics-based method and system for predicting the onset of human diseases using medical imaging and advanced machine learning techniques. This non-invasive approach combines Convolutional Neural Networks (CNNs) with pseudo-color transformation in the CIELAB color space to enhance early disease detection. The method begins by acquiring grayscale medical images from diagnostic techniques such as CT, MRI, or X-ray, followed by CNN-based feature extraction to identify clinically relevant regions of interest. These regions are then converted into pseudo-color representations using the CIELAB color space, improving tissue contrast and visualization of subtle abnormalities. A machine learning classifier is applied to the pseudo-colored images to predict the likelihood of disease onset, generating an output report that includes a heatmap, probability score, and diagnostic recommendations. The invention offers a fully automated process that facilitates early detection, improved visualization, and personalized diagnostics, providing a versatile solution for various medical conditions.

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