Analysis and Classification of H&E-Stained Oral Cavity Tumour Gradings Using Convolution Neural Network

Abstract

Upper Aero Digestive Tract (UADT) cancer is one of the most common cancer types in any gender. Early detection and diagnosing such type of cancer will reduce the risk of death in human. In this paper we addressed the state-of-the-art solutions for two major pathological constraints like, dysplasia type tumour grading and tumour grade classification with artifact's present in biopsy images. A new Whole Slide and patch-based CNN model was proposed which involves different pre-processing techniques to detect and normalize the stain artifacts present in biopsy tissue samples. The proposed CNN model is developed to classify different oral cavity tumour sites and its gradings automatically. It was observed that the proposed CNN model achieved best accuracy of 98 % with zero classification error rate. These findings will throw a light for pathologists to handle digital biopsy images with artifacts and also will classify dysplasia type tumour grades automatically.

An Improved DCNN Classification based on a Modified U-Net Segmentation Approach for Ovarian Cancer

“…CT scans are therefore applied for the categorization and prognosis of ovarian carcinomas in current medications. Practically, CNN is widely known for the categorization and prognosis of various kinds of diseases with the help of CT scans including the nervous system, lung, epidermis, and so on [12][13][14]. On the other hand, there is no appropriate classification framework to detect and treat ovarian carcinoma from CT scans.…”