Computer-aided diagnosis (CAD) of the skin disease based on an intelligent classification of sonogram using neural network

Kia, Shabnam; Setayeshi, Saeed; Shamsaei, Mojtaba; Kia, Mohammad

doi:10.1007/s00521-012-0864-y

Cited by 18 publications

(20 citation statements)

References 9 publications

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“…Parameters from terahertz pulse images of BCC lesions were retrieved and used for its distinction from healthy skin, using SVM algorithms . Kia et al classified healthy skin, melanoma, BCC and benign lesion' sonograms with multilayer perceptron (ANN), achieving SN of 98% and SP of 5%. Even though the authors emphasize the importance of high sensitivity, the low specificity obtained is not acceptable, since unnecessary patient stress would be caused, due to the high number of false positives.…”

Section: Resultsmentioning

confidence: 99%

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The role of AI classifiers in skin cancer images

Magalhães

Mendes

Vardasca

2019

Skin Research and Technology

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Background The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.

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Section: Resultsmentioning

confidence: 99%

“…Parameters from terahertz pulse images of BCC lesions were retrieved and used for its distinction from healthy skin, using SVM algorithms. 73 Kia et al 74…”

Section: Other Imaging Modalitiesmentioning

confidence: 99%

The role of AI classifiers in skin cancer images

Magalhães

Mendes

Vardasca

2019

Skin Research and Technology

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“…Currently detection of the malignancy of skin lesions, is doing by dermatologists based on their professional experience using the pathologic results from the skin biopsy of the suspicious area; and in some cases, meanwhile the skin biopsy of the suspected area, they also recommend the sonography imaging in order to inspect the skin tissue better and in more details . Sonography is a noninvasive method by which radiologists try to capture unusual symptoms in the skin sonograms . Due to the complication of sonograms in appearance, the diagnosis of malignant skin depends on the self‐experience of the dermatologist.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the complication of sonograms in appearance, the diagnosis of malignant skin depends on the self‐experience of the dermatologist. This means that in most cases, the early symptoms of malignant lesions seems to be normal, and ignored . This causes the many false detection.…”

Section: Introductionmentioning

confidence: 99%

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Early diagnosis of skin cancer by ultrasound frequency analysis

Kia

Setayeshi

Pouladian

et al. 2019

J Applied Clin Med Phys

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The diagnosis of cancer by modern computer tools, at the very first stages of the incident, is a very important issue that has involved many researchers. In the meantime, skin cancer is a great deal of research because many people are involved with it. The purpose of this paper is to introduce an innovative method based on tissue frequency analyzes to obtain the accurate and real‐time evaluation of skin cancers. According to the Biological resonance theory, body cells have natural and unique frequencies based on their biological fluctuations, which, if the structure, profile and cellular status change, its frequency also varies. This concept and theory is considered as the basis for analyzing skin tissue health in the proposed method. Reflected ultrasound waves from tissue have been processed and studied based on frequency analysis as a new method for early detection and diagnosis of accurate location and type of skin diseases. The developed algorithm was approved through 400 patients from CRED; its ability to evaluate benign and malignant skin lesions was shown (AUC = 0.959), with comparable clinical precision; as for the selected threshold, sensitivity, and specificity were 93.8% and 97.3%, respectively. Therefore, this method can detect skin malignancy with an accurate, noninvasive and real‐time procedure.

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