Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier

Abstract

There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

“…Both the algorithms achieved an approximately 85% precision and F1-score, whereas our model achieved more than 99%. With the same 3 different kidney planes for 4 classes, 99.44% was achieved by the authors of [30]. They used a DenseNet algorithm, and their model had 19.82 million more parameters than our model.…”

“…The results indicated that their YOLOv7 architecture design outperformed the YOLOv7 tiny architecture design, achieving a mAP50 of 0.85, a precision of 0.882, a sensitivity of 0.829, and an F1-score of 0.854. A study by Abdalbasit et al [30] focused on identifying the three types of kidney abnormalities (stones, cysts, and tumors) using AI techniques on a dataset of over 12,000 CT images. A hybrid approach of pre-trained models and machine-learning algorithms was used, and the Densenet-201 model and random-forest classification yielded an accuracy rate of 99.44%.…”

“…DL models have been applied for a wide range of concerns, such as COVID-19 [17], brain tumors [18], radiation therapy [19], blood clotting [20], heart diseases [21], liver cancer [22], fin-tech [23], intrusion detection [24], vibration signals [25], organic pollutant classification [26], agriculture [27], and steganography [28]. Recent research has shown that DL models can be used successfully for diagnosing kidney abnormalities [29][30][31][32][33]. However, in diagnosing kidney abnormalities, the lightweight models [33] have achieved low accuracy, as compared to the more complex models [29][30][31][32].…”

“…Recent research has shown that DL models can be used successfully for diagnosing kidney abnormalities [29][30][31][32][33]. However, in diagnosing kidney abnormalities, the lightweight models [33] have achieved low accuracy, as compared to the more complex models [29][30][31][32]. Therefore, a lightweight customized DL model could be considered as an alternative approach to achieve higher accuracy.…”

Implementation of clinical diagnosis system for chronic kidney disease using deep learning algorithms

“…Through testing on a dataset of 150 photos, the suggested classifier is compared to the other rival machine learning classifiers and provides a higher accuracy of 96.67%. [36] this study uses a dataset of 12,446 computational thinking (CT) urogram and whole abdomen images to focus on kidney stones, cysts, and tumors-the three most common types of renal illness. The goal is to advance the field of artificial intelligence research while developing a kidney disease diagnosis system that is artificial intelligent AI-based.…”