A Heteromorphous Deep CNN Framework for Medical Image Segmentation Using Local Binary Pattern

Abstract

Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor's aggressiveness and grading system. Because of their strong resemblance to non-mitotic nuclei and heteromorphic form, automated evaluation of mitotic nuclei is difficult. This study presents the BreastUNet, a new heteromorphous Deep Convolutional Neural Network (CNN) with feature grafting approach for analysing mitotic nuclei in breast histopathology images. In the first stage, the proposed method identifies probable mitotic patches in histopathological imaging regions, and in the second stage, the proposed model classifies these patches into mitotic and non-mitotic nuclei. For the building of a heteromorphous deep CNN, four distinct deep CNNs are developed and used as the basis CNN model. Deep CNNs with various architectural designs capture the structural, textural, and morphological aspects of mitotic nuclei. The performance of the proposed BreastUNet model is compared to those of state-of-the-art CNNs. The proposed model looks superior on the test set, with an F1 score of 0.95, Sensitivity and Specificity is 0.95 and area under the precision curve of 0.95. The recommended hybrid high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.