Diagnosis of Skin Melanoma Cancer using Image Based Computer-Aided Diagnosis System from Dermoscopic Images

Skin malignant growth is the most widely recognized, everything being equal. Between 40 to 50 percent of all disease cases analyzed each year are skin malignant growth. Melanomas represent just four percent of all skin malignant growth cases yet are undeniably more perilous. Of all skin disease-related passing’s, 79 percent are from melanoma. Skin disease can be relieved if distinguished early. To appropriately distinguish melanoma, there is a need for a skin test. This is an obtrusive method and is the reason there is a requirement of a conclusion framework that can annihilate the skin test strategy emerges. We proposed to build up a Computer-Aided System that is equipped for ordering a skin injury as threatening or favorable by utilizing the ABCD rule which represents Asymmetry, Border, Color, Diameter of the skin sore. Further, the preprocessed pictures are portioned and commotions are taken out from the Dermoscopic pictures for instance hair and air bubbles. Also, finally, by utilizing a classifier, the proposed system identifies the pictures as favorable or harmful.

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

Neural Computing and Applications ◽

10.1007/s00521-012-0864-y ◽

2012 ◽

Vol 22 (6) ◽

pp. 1049-1062 ◽

Cited By ~ 10

Author(s):

Shabnam Kia ◽

Saeed Setayeshi ◽

M. Shamsaei ◽

Mohammad Kia

Keyword(s):

Neural Network ◽

Skin Disease ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis

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Computer Aided Diagnosis of Melanoma Skin Cancer using Clinical Photographic Images

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v10i8.1474 ◽

2013 ◽

Vol 10 (8) ◽

pp. 1922-1929 ◽

Cited By ~ 2

Author(s):

Mai S. Mabrouk ◽

Mariam Sheha ◽

Amr A. Sharawy

Keyword(s):

Skin Cancer ◽

Prediction Accuracy ◽

Computer Aided Diagnosis ◽

Clinical Images ◽

Diagnosis System ◽

Photographic Images ◽

Melanoma Diagnosis ◽

Computer Aided ◽

Aided Diagnosis ◽

Melanoma Skin

Melanoma is considered as one of the most malignant, metastatic and dangerous form of skin cancer that may cause death. The curability and survival of this type of skin cancer depends directly on the diagnosis and removal of melanoma in its early stages. The accuracy of the clinical diagnosis of melanoma with the unaided eye is only about 60% depending only on the knowledge and experience that each doctor has accumulated. The need to the Computer-Aided Diagnosis system (CAD) is increased to be used as a non-invasive supporting tool for physicians as a second opinion to increase the accuracy of detection, as well contributing information about the essential optical characteristics for identifying them. The ultimate aim of this research is to design an automated low cost computer aided diagnosis system of melanoma skin cancer to increase system flexibility, availability. Also, investigate to what extent melanoma diagnosis can be impacted using clinical photographic images instead of using dermoscopic ones, regarding that both are applied upon the same automatic diagnosis system. Texture features was extracted from 140 pigmented skin lesion (PSL) based on Grey level Co-occurrence matrix (GLCM), effective features are selected by fisher score ranking and then classified using Artificial Neural Network (ANN), the whole system is processed through an interactive Graphical User Interface (GUI) to achieve simplicity. Results revealed the high performance of the proposed CAD system to discriminate melanoma from melanocytic skin tumors using texture analysis when applied on clinical photographic images with prediction accuracy of 100 % for the training phase and 91 % for the testing phase. Also, results indicated that using this type of images provides high prediction accuracy for melanoma diagnosis relevant to dermoscopic images considering that photographic clinical images are acquired using less expensive consumer which exhibit a certain degree of accuracy toward the edges of our field of view.

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Computer-aided Diagnosis of Melanoma: A Review of Existing Knowledge and Strategies

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191210104141 ◽

2020 ◽

Vol 16 (7) ◽

pp. 835-854

Author(s):

Ananjan Maiti ◽

Biswajoy Chatterjee ◽

Amira S. Ashour ◽

Nilanjan Dey

Keyword(s):

Skin Diseases ◽

Imaging Techniques ◽

Computer Aided Diagnosis ◽

New Techniques ◽

Melanoma Cancer ◽

Cad System ◽

Computer Aided ◽

Future Direction ◽

Similar Kind ◽

Aided Diagnosis

Computer-aided diagnosis (CAD) systems are the best alternative for immediate disclosure and diagnosis of skin diseases. Such systems comprise several image processing procedures, including segmentation, feature extraction and artificial intelligence (AI) based methods. This survey highlights different CAD methodologies for diagnosing Melanoma and related skin diseases. It has also discussed types, stages, treatments and various imaging techniques of skin cancer. Currently, researchers developed new techniques to detect each stage. Extensive studies on melanoma cancer detection were performed by incorporating advanced machine learning. Still, there is a high need for an accurate, faster, affordable, portable methodology for a CAD system. This will strengthen the work in related fields and address the future direction of a similar kind of research.

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Computer-Aided Diagnosis of Melanoma Skin Cancer: A Review

Advances in Data and Information Sciences - Lecture Notes in Networks and Systems ◽

10.1007/978-981-10-8360-0_6 ◽

2018 ◽

pp. 63-73 ◽

Cited By ~ 4

Author(s):

Puneet Kumar Goyal ◽

Nirvikar ◽

Mradul Kumar Jain

Keyword(s):

Skin Cancer ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis ◽

Melanoma Skin

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Computer-aided Diagnosis and Medical Decision Support are not Synonymous

Methods of Information in Medicine ◽

10.1055/s-0038-1634612 ◽

1995 ◽

Vol 34 (04) ◽

pp. 369-370 ◽

Cited By ~ 2

Author(s):

F. T. de Dombal

Keyword(s):

Decision Support ◽

Computer Aided Diagnosis ◽

Medical Decision ◽

Medical Decision Support ◽

Computer Aided ◽

Aided Diagnosis

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Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

Methods of Information in Medicine ◽

10.1055/s-0038-1636061 ◽

1972 ◽

Vol 11 (01) ◽

pp. 32-37 ◽

Cited By ~ 10

Author(s):

F. T. DE DOMBAL ◽

J. C. HORROCKS ◽

J. R. STANILAND ◽

P. J. GUILLOU

Keyword(s):

Pattern Recognition ◽

Computer System ◽

Computer Aided Diagnosis ◽

Clinical Interview ◽

Computer Performance ◽

Pattern Size ◽

Computer Aided ◽

Computer Based ◽

Aided Diagnosis ◽

Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

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