Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images

Bayğın, Mehmet; Yaman, Orhan; Barua, Prabal Datta; Doğan, Şengül; Tuncer, Türker; Acharya, U. Rajendra

doi:10.1016/j.artmed.2022.102274

Cited by 54 publications

(27 citation statements)

References 15 publications

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“…Baygin et al (Baygin et al, 2022) aimed to classify patients with or without kidney stones using CT images. The used data set consists of CT images of 433 patients.…”

Section: Related Workmentioning

confidence: 99%

“…The main purpose of AI assisted diagnosis is to develop systems that enable radiologists to assist in the detection of disease. Examples of these systems are kidney segmentation (D. T. Lin et al, 2006), cyst segmentation (Z. , kidney stone segmentation (Baygin et al, 2022), systems that classify the computerized tomography image according to its condition (Z. .…”

Section: Introductionmentioning

confidence: 99%

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Böbrek Hastalıkları için Açıklanabilir Yapay Zeka Destekli Derin Öğrenmeye Dayalı Bir Tespit ve Tahmin Modeli

BAYRAM

GURKAN

Budak³

et al. 2022

EJOSAT

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Kidney diseases are one of the most common diseases worldwide and cause unbearable pain in most people. In this study aims to detecting the cyst and stone in the kidney. For the this purpose, YOLO architecture designs were used for detection of kidney, kidney cyst and kidney stone. The YOLO architecture designs were supported by the explainable artificial intelligence (xAI) feature. CT images in three classes, namely 72 kidney cysts, 394 kidney stones and 192 healthy kidneys were used in the performance analysis part of the YOLO architecture designs. As a result, YOLOv7 architecture design outperformed the YOLOv7 Tiny architecture design. YOLOv7 architecture design achieved the mAP50 of 0.85, precision of 0.882, sensitivity of 0.829 and F1 score of 0.854. Consequently, deep learning based xAI assisted computer aided diagnosis (CAD) system was developed for diagnosis of kidney diseases.

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“…Baygin et al (Baygin et al, 2022) aimed to classify patients with or without kidney stones using CT images. The used data set consists of CT images of 433 patients.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Böbrek Hastalıkları için Açıklanabilir Yapay Zeka Destekli Derin Öğrenmeye Dayalı Bir Tespit ve Tahmin Modeli

BAYRAM

GURKAN

Budak³

et al. 2022

EJOSAT

View full text Add to dashboard Cite

show abstract

“…In the last of 2020, a new patch-based deep learning model was presented, named ViT (vision transformer) [ 8 ]. ViT obtained higher classification performance than popular CNNs [ [53] , [54] , [55] , [56] ]. Swin transformer [ 11 ] is an improved version of the ViT and uses variable-sized patch division operations.…”

Section: Introductionmentioning

confidence: 99%

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Tuncer

Barua

Doğan

et al. 2023

Informatics in Medicine Unlocked

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“…Machine learning and artificial intelligence models are commonly used in the biomedical and bioinformatics sciences to solve classification problems [ 16 , 17 , 18 ]. Therefore, we were motivated to develop a computationally lightweight machine learning model for automated SARS-CoV-2 versus Influenza-A diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences

et al. 2022

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SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare’s Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

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Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images

Cited by 54 publications

References 15 publications

Böbrek Hastalıkları için Açıklanabilir Yapay Zeka Destekli Derin Öğrenmeye Dayalı Bir Tespit ve Tahmin Modeli

Böbrek Hastalıkları için Açıklanabilir Yapay Zeka Destekli Derin Öğrenmeye Dayalı Bir Tespit ve Tahmin Modeli

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences

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