A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Mahmoud Badawy; Abdulqader M Almars; Hossam Magdy Balaha; Mohamed Shehata; Mohammed Qaraad; Mostafa Elhosseini

doi:10.3389/fmed.2023.1106717

A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Front Med (Lausanne). 2023 Apr 5:10:1106717. doi: 10.3389/fmed.2023.1106717. eCollection 2023.

Authors

Mahmoud Badawy^{1

2}, Abdulqader M Almars³, Hossam Magdy Balaha^{1

4}, Mohamed Shehata⁵, Mohammed Qaraad^{6

7}, Mostafa Elhosseini^{1

3}

Affiliations

¹ Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
² Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia.
³ College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia.
⁴ Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States.
⁵ Department of Computer Science and Engineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States.
⁶ Department of Computer Science, Faculty of Science, Amran University, Amran, Yemen.
⁷ TIMS, Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco.

Abstract

Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).

Keywords: AI-based diagnosis; Sparrow Search Algorithm (SpaSA); convolutional neural network (CNN); metaheuristic optimization; renal diseases (RD); transfer learning (TL).