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

• Published in: Interdisciplinary sciences : computational life sciences Vol. 16; no. 4; pp. 907 - 925
• Main Authors: Maqsood, Faiqa, Zhenfei, Wang, Ali, Muhammad Mumtaz, Qiu, Baozhi, Rehman, Naveed Ur, Sabah, Fahad, Mahmood, Tahir, Din, Irfanud, Sarwar, Raheem
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Biomedical and Life Sciences; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computed tomography; Computer Appl. in Life Sciences; Computer applications; Computer vision; Computing costs; Cysts; Datasets; Deep Learning; Dietary supplements; Excess water; Feature extraction; Health Sciences; Humans; Image acquisition; Image filters; Image Processing, Computer-Assisted - methods; Kidney cancer; Kidney diseases; Kidney Diseases - diagnostic imaging; Kidney stones; Kidneys; Life Sciences; Machine learning; Mathematical and Computational Physics; Medical imaging; Medicine; Nephrology; Neural networks; Neural Networks, Computer; Optimization techniques; Original; Original Research Article; Real time; Renal failure; Statistics for Life Sciences; Task complexity; Theoretical; Theoretical and Computational Chemistry; Tomography; Tomography, X-Ray Computed - methods; Tumors; Water purification; Wavelet transforms; SpinalNet; Efficient neural network; Computed tomography; Median filter; Zeiler and Fergus network
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-024-00649-4

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. Graphical Abstract