A Portable Thermogram based Non-contact Non-invasive Early Breast-Cancer Screening Device

Thermogram (infrared) based non-contact non-invasive screening device for breast cancer is presented. To enable a home-based patient-comfort portable breast cancer screening device, a FLIR thermal imaging camera, with the best of four (BoF) features based on the proposed novel methodology and the support vector machine (SVM) learning classifier is exploited. The 4-dimension Feature Vector (FV) is computed using the segmented image, grey Level co-occurrence matrix (GLCM) and run-length matrix (RLM) calculation. To ensure hardware optimization, the proposed multiplexed GLCM, RLM and SVM implementation realizes an area reduction of 30% compared to the conventional with minimal overhead in the system speed requirement. A Linear SVM is utilized to decide between malignant and benign based on the FV. The system is implemented on FPGA and experimentally verified using the patients from the Mastological Research database. The proposed breast cancer screening processor targets a portable home environment and achieves the sensitivity and specificity of 79.06% and 88.57%, respectively.