Developing a convolutional neural network for classifying tumor images using Inception v3

Abstract

Deep learning algorithms rely on digital pathology to classify tissue tumors, where the whole tissue slides are digitized and imaged. The produced multi-resolution whole slide images (MWSIs) are with high resolution that may range from about 100,000 to 200,000 pixels. MWSIs are often stored in a multi-resolution configuration to simplify the processing of images, navigation, and efficient exposition. This work develops a network for classifying MWSIs that require high memory employing a deep neural Inception-v3 architecture. This work employs the MWSIs from Camelyon16, which is around 451 GB in size of Challenge dataset from two independent sources including 400 MWSIs as a total of lymph nodes. The training dataset contains 111 MWSIs of tumor tissue and lymph nodes and 159 WSIs of normal lymph nodes. The developed model uses sample-based processing to train extensive MWSIs employing the MATLAB platform. The model introduces transfer learning techniques with an Inception-v3-based architecture to categorize separate samples as a tumor or normal. Therefore, the main aim here is to achieve two-classes binary segmentation containing normal and tumor. This includes creating a new fully connected layer for the Inception-v3 architecture with two classes and compensating new layers instead of the original final fully-connected layers. The results obtained demonstrated that the heatmap visualization can recognize the boundary coordinates of ground truth as sketchy Region Of Interest (ROI), where the green boundary represents the normal regions and the tumor area with red boundaries. The proposed Inception v3 Convolutional Neural Network (CNN) architecture can achieve more than 92.8 % accuracy for such MWSIs dataset to categorize brain tumors into normal and tumor tissue