Intelligent Diagnostic System for Papillary Thyroid Carcinoma

Due to exponential growth and high diversity of DICOM images in healthcare industry, medical image mining has gained prominent attention because extraction of useful and effective patterns is one of the major problems in DICOM images. For instance, differentiating between the mimic, mix and complex patterns of thyroid papillary carcinoma (PTC) and other cancers in FNAC images is really challenging since it requires in-depth study of cells and tissues. In order to reduce the chances of misdiagnosis of thyroid cancer and to discernment the mimic lesions of papillary thyroid carcinoma (Small cell carcinoma may mimic insular carcinoma). This article proposes a framework, so called (IDSPTC) Intelligent Diagnostic System for papillary thyroid carcinoma, which offers a systematic way to classify papillary and non-papillary structures by using AI base techniques. In first phase, we prepared own dataset due to unavailability of training and testing datasets in literature. We applied our proposed algorithm “DICOM_Segs graph-Sag” to extract the useful movement and coordinate based patterns of nuclei and constructed decision model using ANN (artificial neural network) to identify the papillary structures by analysing nuclei coordinates; finally we performed test model and performance evaluation to measure the classification accuracy using confusion matrix, precision and recall measures and visualized the ROC curve for papillary and non-papillary classes. The measured accuracy of our proposed system is 90.32% with 10-k fold cross validation.