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

Abstract

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.

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

“…They used a DenseNet algorithm, and their model had 19.82 million more parameters than our model. Though the results obtained by VGG-DN-KNN using 5-fold cross-validation was 100% in the work performed by the authors of [31], it only achieved approximately 96% for VGG16-NB and Densenet121-KNN. All the algorithms implemented had a high number of parameters.…”

“…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%. Venkatesan et al [31] proposed a framework for classifying renal CT images as healthy or malignant using a pre-trained deep-learning approach. To improve its accuracy, they employed a threshold-filter-based pre-processing scheme to eliminate artifacts.…”

“…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.…”

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

“…The utilization of radionics properties in a categorization strategy can efficiently categorize most malignancies. According to [11] Offer an organized structure for categorizing renal computerized tomography (CT) images either as normal or malignant. The framework employs deep features extracted from pre-trained models including VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201.…”