Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet

Abstract

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney function and ultimately leads to complications such as chronic kidney disease, kidney failure, and other renal disorders. Combining patient metadata with computed tomography (CT) images is essential to accurately and timely diagnosing such complications. Deep Neural Networks (DNNs) have transformed medical fields by providing high accuracy in complex tasks. However, the high computational cost of these models is a significant challenge, particularly in real-time applications. This paper proposed SpinalZFNet, a hybrid deep learning approach that integrates the architectural strengths of Spinal Network (SpinalNet) with the feature extraction capabilities of Zeiler and Fergus Network (ZFNet) to classify kidney disease accurately using CT images. This unique combination enhanced feature analysis, significantly improving classification accuracy while reducing the computational overhead. At first, the acquired CT images are pre-processed using a median filter, and the pre-processed image is segmented using Efficient Neural Network (ENet). Later, the images are augmented, and different features are extracted from the augmented CT images. The extracted features finally classify the kidney disease into normal, tumor, cyst, and stone using the proposed SpinalZFNet model. The SpinalZFNet outperformed other models, with 99.9% sensitivity, 99.5% specificity, precision 99.6%, 99.8% accuracy, and 99.7% F1-Score in classifying kidney disease.

Keywords: Computed tomography; Efficient neural network; Median filter; SpinalNet; Zeiler and Fergus network.

Fig. 1

Block diagram of SpinalZFNet used for kidney disease classification

Fig. 2

Structure of SpinalNet Model

Fig. 3

Structure of ZFNet model

Fig. 4

Experimental image outcomes. a Input, b pre-processed, c segmented, d translation, e rotation, and f padding augmented CT image

Fig. 5

Training and validation accuracy and loss curves on the CT images of the SpinalZFNet model

Fig. 6

Performance analysis of SpinalZFNet using K-fold (a sensitivity, b specificity, c accuracy) and learning set, (d sensitivity, e specificity, and f accuracy)

Fig. 7

Comparative analysis of SpinalZFNet using K-fold: sensitivity, specificity, accuracy, and F1-score

Fig. 8

Comparative analysis of SpinalZFNet using learning set: sensitivity, specificity, accuracy, and F1-score

Fig. 9

Performance analysis using confusion matrix of SpinalZFNet with state-of-the-art models and ROC curves for SpinalZFNet

Fig. 10

A sample visualizations of the ZFNet model