Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Dhruv, Bhawna; Mittal, Neetu; Modi, Megha

doi:10.4108/eai.31-5-2021.170009

Cited by 9 publications

(7 citation statements)

References 47 publications

(49 reference statements)

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“…Data type a [21,[24][25][26][27][28][29][30][31]34,36,40,42,44] 14 (47) Radiology images [18,19,22,23,32,33,35,[37][38][39]41,43] 12 (40) Clinical data [16,17,20,23,32,35,39,41…”

Section: References Studies N (%) Featuresmentioning

confidence: 99%

“…Validation approach a [18,[20][21][22]28,31,33,37,38,40] 10 (33) External validation [16,17,19,[23][24][25]28,34,35,37,39] 10 (33) K-fold cross-validation [19,22,39,41,43] 5 (17) Hold-out cross-validation [31,35] 2 (7) Leave-one-out cross-validation [29] 1 (3) Shuffle-split cross-validation [26,32,36,42] 4 (13) Not reported…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

“…Confusion matrix [18,20,35,40,45] 5 (17) Reported [16,17,19,21,[23][24][25][26][27][28][29][30][31][32][33][34][36][37][38]41,42,44]…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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Background Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. Objective This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. Methods A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. Conclusions This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

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“…Data type a [21,[24][25][26][27][28][29][30][31]34,36,40,42,44] 14 (47) Radiology images [18,19,22,23,32,33,35,[37][38][39]41,43] 12 (40) Clinical data [16,17,20,23,32,35,39,41…”

Section: References Studies N (%) Featuresmentioning

confidence: 99%

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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show abstract

“…The currently employed conventional image segmentation models consume considerable computational time and power, as they perform every operation for each pixel in the image [ 86 ]. Further, the resultant processed image quality also lacks quality, thereby necessitating the development of more robust tools for AI-driven tools for image segmentation and processing that may provide a better diagnostic accuracy [ 87 ]. In an interesting study [ 88 ], about 19,500 non-contrast CT scan images, acquired from 469 scans, were segmented using CNNs and the mean pancreatic tissue density, in terms of the Hounsfield unit (HU), as well as the pancreatic volume, were computed using the CNN algorithm.…”

Section: Computed Tomographymentioning

confidence: 99%

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Hameed

Krishnan

2022

Cancers

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Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians’ treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

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“…In their study, Dhruv et al (17) review the edge detection techniques most commonly used in the medical field, emphasizing the operators based on Roberts, Sobel, Prewitt and Canny, where they highlight for the latter algorithm the use of the Gaussian filter as a method of image defocusing, to subsequently obtain the magnitude and orientation of the gradient of the image. Likewise, Devkota et al (18) developed a method for the detection of early-stage tumors using mathematical morphology reconstruction, where they initially subject the image to preprocessing accompanied by median filtering, and where the morphological operation used is erosion to complement initial segmentation stages.…”

Section: Introductionmentioning

confidence: 99%

approach to edge detection in medical imaging through histogram analysis and morphological gradient

Rondón¹,

Carvajal

Casadiego³

et al. 2022

inycomp

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Edge detection takes importance in image processing systems for computer-aided diagnosis, wheresharp changes in pixel intensity are analyzed to obtain fast and accurate information about regions ofinterest to the specialist. A method for feature enhancement and edge detection in medical imageswas developed using image processing by analyzing the pixel distribution histogram andmorphological gradient operation. Images from the MINI MIAS dataset and the COVID-CT datasetwere used. The method is based on image processing and is applied to mammography and chest CTimages, where blur filtering steps are accompanied by morphological gradient filtering, in addition toobtaining the threshold for edge detection by analyzing the point of maximum pixel concentrationaccording to the distribution histogram. The processing is presented in a graphical user interfacedeveloped in Python language. The method is validated by comparison with other edge detectiontechniques such as the Canny Algorithm, and with deep learning methods such as Holistically-NestedEdge Detection. The proposed method improves image quality in both mammograms and CT scanscompared to other techniques. It also presents the best performance considering internal and externaledge detection, as well as an average response time of 1.054 seconds and 2.63 % of Central Processing Unit requirement. The developed system is presented as a support tool for use in computer-aideddiagnosis processes due to its high efficiency in edge detection.

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Early and Precise Detection of Pancreatic Tumor by Hybrid Approach with Edge Detection and Artificial Intelligence Techniques

Cited by 9 publications

References 47 publications

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

approach to edge detection in medical imaging through histogram analysis and morphological gradient

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