In this chapter, we present an enhanced and automated methodology for the detection of tumour cells in fixed biopsy samples. The metamaterial formalism (MMF) approach that recognises tumour areas in tissue samples is enhanced by providing an advanced technique to digitise mouse biopsy images. Moreover, we are taking a step forward by considering cancerous tissues as the highly disordered anisotropic media. We use the classical Maxwell-Garnett technique, which is a perfect tool to evaluate an effective medium of the sample analytically without human intervention by performing an experimental analysis to measure the parameters of the sample. It is worth mentioning that laboratory measurements of the effective properties are not needed in this case. In this regard, the presented technique allows for the creation of the phantom tissue models for further usage in clinical applications. A colour-based segmentation technique based on the K-means clustering method is used allowing for a precise segmentation of the cells composing the biological tissue sample. Errors occurring at the tissue digitisation steps are detected by applying MMF. By doing so, we end up with a robust, fully automated approach with no need for human intervention, ready for clinical applications. The proposed methodology consists of three major steps: digitisation of the biopsy image, analysis of the biopsy image, and modelling of the disordered metamaterial. It is worth mentioning that the technique under consideration allows for cancer stage detection. Moreover, early-stage cancer diagnosis is possible by applying MMF.