Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Abstract

Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions

“…Regardless of the effectiveness of the feature extraction procedure, the accuracy and quality of the extracted feature can only be achieved if a suitable classifier is employed in the classification design. [14] . displays the findings from the relevant tumor classification studies.…”

“…Precise delineation of Kidney Tumors is crucial for later diagnosis and treatment strategizing. [14]. DL offers the benefit of autonomously generating the features necessary to differentiate kidneys and tumors from the remaining parts of the scan [30] .…”

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

“…Maha et.al. [21] this review article reviews the research on radiology imaging related to deep learning and summarizes the scientific progress that has been achieved, as well as determining the ways and techniques that researchers have used in the previous years in order to diagnose kidney tumors from medical images and identify future avenues, whether in terms of technological developments or applications. Abubaker et.al.…”

Preliminary Construction of Prognostic Risk Early Warning Model for Renal Cell Carcinoma with Type 2 Diabetes Mellitus Based on SMOTE Algorithm

“…In this study, a small sample group is a poor prognosis group and the sample multiple (n) should be increased to represent the ratio of the number of patients in the good prognosis group to the number of patients in the poor prognosis group (rounded to the nearest integer). The specific process of SMOTE oversampling [5] is; calculate the k nearest neighbors of each sample in the infection group; randomly select a sample j from the k nearest neighbors of the sample point i in the infection group; calculate sample i and the difference Q of all variable attributes of sample j; randomly generate a value R between 0 and 1; generate a new sample=Sample i+R×Q; repeat steps to until the number of patients in the poor prognosis group reaches n times; repeat steps to until all the sample variables of the poor prognosis group have been processed. The data set expanded by this method is essentially to perform intra-class sample interpolation on the minority class samples, without changing the original spatial boundary of the samples, and has high reliability and validity.…”