|
Worldwide, a considerable number of female cancer cases are attributed to breast cancer, making it a prevalent and serious problem. As diagnoses surge, the traditional approach of manual histological assessment is becoming increasingly inefficient. So, to expedite diagnosis and eliminate the need for specialised expertise, researchers are turning to automated alternatives. Polarization-Sensitive Optical coherence tomography (PS-OCT) emerges as a promising tool, offering a rapid alternative to traditional histology. It stands out by exploiting the polarization of reflected light to boost image contrast. By evaluating polarized backscattered light, the PS-OCT system is able to detect birefringence in cancerous tissue, indicative of collagen changes associated with cancer. The main focus of this study is the development of an automated Full-field PS-OCT (FF-PS-OCT) system for the diagnosis of breast cancer. The system recorded 220 sample images in order to extract phase information. The birefringence and degree of polarization uniformity information is calculated from the recorded phase images. Different features have been extracted from birefringence and degree of polarization uniformity images to train an ensemble model that has been validated by the technique for order preference by similarity to ideal solution (TOPSIS) to distinguish between normal and malignant breast tissue. The multi-layer ensemble model demonstrates enhanced performance in terms of recall and precision, achieving remarkable metrics on the testing dataset: 92.3% precision, 90% recall, 91.1% F-score, and 79.7% Matthews correlation coefficient. These preliminary results underscore the potential of FF-PS-OCT as rapid, non-contact, and label-free imaging tool. Its implementation shows potential in empowering medical professionals with the insights needed for making informed decisions during interventions.
|
