Feature assisted cervical cancer screening through DIC cell images

The mortality rate of cervical cancer is increasing alarmingly. Conventional cytological methods are not always efficient to diagnose cancer at an early stage. Several label-free, quantitative screening approaches are emerging rapidly for fast and accurate detection of cervical cancer. Differential interference contrast (DIC) imaging is one of such label-free methods for the detection of cellular abnormality. The combination of DIC imaging and prediction algorithm enables the development of an efficient computer-aided diagnosis (CAD) system for cervical cancer detection at an early stage. In the present study, the DIC dataset is categorized into 2-classes (abnormal and normal) and 3-classes (normal, pre-cancer, and squamous cell carcinoma). After segmentation of the cells using the modified valley-based Otsu’s thresholding method, three classifiers, namely support vector machine (SVM), multilayer perceptron (MLP), and k-nearest neighbour (k-NN) are applied. Further, to improve the classification performances, principal component analysis (PCA) is applied for feature selection. The experimental results reveal that the SVM classifier has the greatest accuracy of 0.97 (2-class classification) and 0.90 (3-class classification).

---

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).