Nuclei Classification in Histopathology Images Using Fuzzy Ensemble of Convolutional Neural Networks

The development of computational pathology and healthcare studies depends on cell nuclei classification. It has several lifesaving uses, particularly in cancer diagnosis. It is helpful in various applications, including disease diagnosis and medicinal therapy. Due to the number of problems, human examination of these image slides is unpleasant and time-consuming. Morphological traits add to the intricacy. We suggested a fuzzy distance ensemble of Convolutional Neural Networks to achieve state-of-the-art nucleus classification performance. This new model is easier to train and makes more accurate predictions using underlying basic classifiers. To examine how well our model worked, we used the PanNuke dataset. Our model’s scores are 0.811 for accuracy, 0.811 for F1, 0.815 for precision, and 0.809 for recall.