Glomerulosclerosis detection with pre-trained CNNs ensemble

Glomerulosclerosis characterizes many conditions of primary kidney disease in advanced stages. Its accurate diagnosis relies on histological analysis of renal cortex biopsy, and it is paramount to guide the appropriate treatment and minimize the chances of the disease progressing to chronic stages. This article presents an ensemble approach composed of five convolutional neural networks (CNNs) - VGG-19, Inception-V3, ResNet-50, DenseNet-201, and EfficientNet-B2 - to detect glomerulosclerosis in glomerulus images. We fine-tuned the CNNs and evaluated several configurations for the fully connected layers. In total, we analyzed 25 different models. These CNNs, individually, demonstrated effectiveness in the task; however, we verified that the union of these five well-known CNNs improved the detection rate while decreasing the standard deviations of current techniques. The experiments were carried out in a data set comprised of 1,028 images, on which we applied data-augmentation techniques in the training set. The proposed CNNs ensemble achieved a near-perfect accuracy of 99.0% and kappa of 98.0%.