An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine

Poovizhi, S.; Tr, Ganesh Babu

doi:10.1590/0001-3765202020190554

Cited by 11 publications

(2 citation statements)

References 16 publications

(6 reference statements)

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“…Around 73% (39/53) of the papers used accuracy as their primary evaluation metric to assess the trained models. The average accuracy value was 86.8%, with a maximum of 98.8% [ 60 ] and a minimum of 67% [ 10 ]. The AUC was reported in 9 studies, with an average score of 87.2%; the highest AUC score was 91.7% [ 41 ], whereas the lowest AUC score was 82.0% [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

“…We believe that having a confusion matrix or the number of TPs, FPs, TNs, and FNs would avoid bias and give a clearer evaluation of how the model behaves with regard to each of the diagnostic classes. From the studies, the top accuracy scores were ~98% [ 21 , 27 , 60 ]. In studies leading to this accuracy, the authors built a two-class classification (benign vs malignant) model using data sets of 200, 356, and 200 images, respectively.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence for Skin Cancer Detection: Scoping Review

Takiddin¹,

Schneider²,

Yang³

et al. 2021

J Med Internet Res

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Background Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Takiddin¹,

Schneider²,

Yang³

et al. 2021

J Med Internet Res

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Artificial intelligence for stepwise diagnosis and monitoring of COVID-19

Liang

Guo

Chen³

et al. 2022

Eur Radiol

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Background Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient’s clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient’s clinical course. Methods CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. Results A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97–0.99), and outperforms the radiologist’s assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice’s coefficient of 0.77. It can produce a predictive curve of a patient’s clinical course if serial CT assessments are available. Interpretation The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient’s clinical course for visualization. Key Points • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist’s assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient’s clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08334-6.

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An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification

Desale¹,

Patil²

2023

The Imaging Science Journal

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An Efficient Skin Cancer Diagnostic System Using Bendlet Transform and Support Vector Machine

Cited by 11 publications

References 16 publications

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19

An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification

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