Histomorphological Spectrum of Neoplastic Lesions of Kidney with a Brief Review of Literature

Modepalli, Nalini; Anantharaj, Jyothi; Shivappa, Naveen; Basavaraj, Shwetha; Barve, Sakshi

doi:10.31782/ijcrr.2021.13707

Cited by 1 publication

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“…Meanwhile, renal tumors are a major cause of morbidity around the world. Renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% percent of all cancers, according to Modepalli et al ( 5 ). Renal cell carcinoma (RCC) is the most frequent type of kidney cancer, accounting for around 2% of cancer-related mortality worldwide ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

“…In the USA, kidney cancer is expected to cause 73,750 new cases and 14,830 deaths in 2020 ( 7 ). Figure 1 ( 5 ) shows the renal Oncocytoma Microscopy and Cut surface. The WHO classification of renal tumors distinguishes 12 different RCC subtypes.…”

Section: Introductionmentioning

confidence: 99%

“…Clear-cell RCC (ccRCC) accounts for around 80% of all RCC cases ( 7 ). As a result, surgeons must look for new microscopic findings in RCC diagnoses and classification ( 5 ). Therefore, renal tumor histological reclassification is critical based on molecular, clinical, and pathological features.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%