Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Abdelrahman, Abubaker; Viriri, Serestina

doi:10.3390/jimaging8030055

Cited by 26 publications

(22 citation statements)

References 144 publications

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“…This study considered the axial-plane RCT slices provided by Islam et al ( 21 ). This dataset consists of both the axial-plane and coronal-plane images with categories, such as cyst, stone, cancer, and healthy.…”

Section: Methodsmentioning

confidence: 99%

“…Along with the above-considered studies, the research by Abdelrahman and Viriri ( 21 ) presents a detailed survey on traditional and deep-learning segmentation of the abnormal fragment in the kidney in RCT images. The research by Wang et al ( 22 ) also presents a thorough evaluation of the deep-learning-supported scheme for biomedical image examination, including the RCT.…”

Section: Related Studiesmentioning

confidence: 99%

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A framework to distinguish healthy/cancer renal CT images using the fused deep features

Rajinikanth

Vincent

Srinivasan

et al. 2023

Front. Public Health

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IntroductionCancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management.MethodsThe proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation.Results and discussionThis experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.

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Section: Methodsmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

A framework to distinguish healthy/cancer renal CT images using the fused deep features

Rajinikanth

Vincent

Srinivasan

et al. 2023

Front. Public Health

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“…Abubaker et.al. [22] the authors of this article, addressed recent advancements in DL-based kidney tumor segmentation systems. They address the many kinds of medical imaging and segmentation methodologies, as well as the evaluation criteria for segmentation results in the segmentation of kidney tumors, emphasizing their constituent parts and tactics.…”

Section: Related Workmentioning

confidence: 99%

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

Qadir

Faiq

2023

KJAR

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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.

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“…In the 1970s and 1990s, medical pictures were analyzed using sequential low-level pixel processing and computational analysis [12]. Some prior researches have included typical machine learning techniques as well as handcrafted attributes to increase the performance of brain stroke lesion segmentation.…”

Section: Related Workmentioning

confidence: 99%

Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks

et al. 2022

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Segmentation of brain regions affected by ischemic stroke helps to overcome the main obstacles in modern studies of stroke visualization. Unfortunately, contemporary methods of solving this problem using artificial intelligence methods are not optimal. Therefore, in the study we consider how to increase the efficiency of segmentation of the stroke focus using computer perfusion imaging using modifications based on UNet. The network was trained and tested using the ISLES 2018 dataset. The publication includes an analysis of the results obtained, as well as recommendations for future research. By choosing the appropriate model parameters, our approach can be easily applied to detect ischemic stroke. We present modified U-Net models with two ResNet blocks as U-Net+ ResNetblock 1 and U-Net +ResNetblock 2, as well as a modified UNet model. Due to the small number of images for training the model, the best results were obtained by applying data preprocessing and object representation approaches, as well as data normalization methods to avoid overfitting. The results show that the modified UNet model is superior to other models in terms of average distance and recall, that are significant parameters for segmentation of the stroke.

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Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Cited by 26 publications

References 144 publications

A framework to distinguish healthy/cancer renal CT images using the fused deep features

A framework to distinguish healthy/cancer renal CT images using the fused deep features

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

Brain Stroke Lesion Segmentation Using Computed Tomography Images based on Modified U-Net Model with ResNet Blocks

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