Computer-assisted detection of subcutaneous melanomas (original) (raw)
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A Survey On Melanoma: Skin Cancer Through Computerized Diagnosis
SSRN Electronic Journal, 2020
This paper examines about a type of skin malignant growth which is melanoma. There are numerous types of skin malignancy, for example, Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma. In which the deadliest type of skin disease is the Melanoma. Demise pace of melanoma has expanded among skin malignant growth patients and it is hazardous. The death rate is highest among among middle aged and elderly individuals. It is seen as risky when it develops beyond the dermis of the skin. This paper deals with a survey on a few computerized analysis procedures for diagnosing melanoma. These procedures extract different parameters, for example, shape, size, surface, shading and different properties of lesions which is utilized for additional exploration. The precise skin affected region which is the skin lesion or area of intrigue will be taken out for automated medical procedure. The ATLAS dataset or PH2 dataset pictures are considered for investigation in the majority of the papers.
A Survey on Computer - Aided Melanoma Skin Cancer Detection
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019
Skin cancers are the most widely recognized types of human malignancies in reasonable skinned populaces. Albeit malignant melanoma is the type of skin cancer with the most noteworthy mortality, the non-melanoma skin cancers are undeniably normal. The frequency of both melanoma and non-melanoma skin cancers is expanding, with the quantity of cases being analyzed multiplying roughly at regular intervals. In this way, early finding of skin cancer can lessen mortality of patients. In this paper we are exploring different procedures for beginning period melanoma skin cancer detection. For skin lesion detection pathologists look at biopsies to make diagnostic appraisal to a great extent in light of cell life systems and tissue conveyance yet in numerous examples it is emotional and frequently prompts impressive changeability. While PC diagnostic apparatuses empower target judgments by making utilization of quantitative measures. This paper audits the prior period and current advances for machine aided skin cancer detection.
Computer-aided diagnosis of melanocytic lesions
Anticancer research
The clinical diagnosis of melanoma could be difficult for a general practitioner and, in some cases, for dermatologists. To enhance and support the clinical evaluation of pigmented skin lesions a computer-aided diagnosis has been introduced. Images of melanocytic lesions (477 total, 42 melanomas and 435 melanocytic nevi) evaluated in epiluminescence microscopy and recorded with x16 magnification were selected. A training set of 22 melanomas and 218 nevi was randomized from the dataset. The test set was formed by the complement (the remaining 20 melanomas and 217 nevi). Furthermore, a set of images consisting of 31 melanomas and 103 nevi was selected to compare the discrimination capacity of three general practitioners and three dermatologists with experience in dermoscopy (2 years), and with the automatic data analysis for the melanoma early detection system (ADAM). Sensitivity and specificity were estimated for observer assessments and computer diagnosis. The entire dataset used to...
Skin cancers are the most widely recognized types of human malignancies in reasonable skinned populaces. Albeit malignant melanoma is the type of skin cancer with the most noteworthy mortality, the non-melanoma skin cancers are undeniably normal. The frequency of both melanoma and nonmelanoma skin cancers is expanding, with the quantity of cases being analyzed multiplying roughly at regular intervals. In this way, early finding of skin cancer can lessen mortality of patients. In this paper we are exploring different procedures for beginning period melanoma skin cancer detection. For skin lesion detection pathologists look at biopsies to make diagnostic appraisal to a great extent in light of cell life systems and tissue conveyance yet in numerous examples it is emotional and frequently prompts impressive changeability. While PC diagnostic apparatuses empower target judgments by making utilization of quantitative measures. This paper audits the prior period and current advances for m...
International Journal of Dermatology, 2006
Background For early melanoma diagnosis, experienced dermatologists have an accuracy of 64–80% using clinical diagnostic criteria, usually the ABCD rule, while automated melanoma diagnosis systems are still considered to be experimental and serve as adjuncts to the naked-eye expert prediction. In an attempt to aid in early melanoma diagnosis, we developed an image processing program with the aim to discriminate melanoma from melanocytic nevi, establishing a mathematical model to come up with a melanoma probability.Methods Digital images of 132 melanocytic skin lesions (23 melanomas and 109 melanocytic nevi) were studied in features of geometry, color, and color texture. A total of 43 variables were studied for all lesions, e.g., geometry, color texture, sharpness of border, and color variables. Univariate logistic regression analysis followed by “−2 log likelihood” test and Spearman's rank correlation coefficient were used to eliminate inappropriate variables, as the presence of multicollinearity among variables could cause severe problems in any stepwise variable selection method. Initially, “−2 log likelihood” and nonparametric Spearman's rho picked five variables to be included in a multivariate model of prediction. The five-variable model was then reduced to three variables and the performance of each model was tested. The “jackknife” method was performed in order to validate the model with the three variables and its accuracy was weighed vs. the five-variable model by receiver-operating characteristics (ROC) curve plotting. It was concluded that the reduced model did not compromise discriminatory power.Results Not all variables contributed much to the model, therefore they were progressively eliminated and the model was finally reduced to three covariates of significance. A predictive equation was calculated, incorporating parameters of geometry, color, and color texture as independent covariates for the prediction of melanoma. The proposed model provides melanoma probability with a 60.9% sensitivity and 95.4% specificity of prediction, an overall accuracy of 89.4% (probability level 0.5), and 8% false-negative results.Conclusions Through a digital image processing system and the development of a mathematical model of prediction, discrimination between melanomas and melanocytic nevi seems feasible with a high rate of accuracy using multivariate logistic regression analysis. The proposed model is an alternative method to aid in early melanoma diagnosis. Expensive and sophisticated equipment is not required and it can be easily implemented in a reasonably priced portable programmable computer, in order to predict previously undiagnosed skin melanoma before histopathology results confirm diagnosis.
Automated Skin Lesion Detection towards Melanoma
EAI Endorsed Trans. Scalable Inf. Syst., 2019
Skin cancer melanoma is one of the most dangerous cancers in the world. It is crucial to diagnose it in initial phases before it invades other organs. However, it requires an efficient and reliable diagnostic computer aided system for early detection. In this research study we aim to detect the skin cancer from two different image datasets. We also present the solution for images that contain disk objects. In initial phase we perform pre-processing, which is followed by segmentation phase. Then candidate dataset is evaluated using different measures such as accuracy, specificity, sensitivity and similarity. Obtained results are compared with results of techniques used in academic literature. We claim that our techniques give better accuracy for cancer detection.
IEEE Journal of Translational Engineering in Health and Medicine, 2015
Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors' subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.
Melanoma skin cancer detection : State of the Art
2013
ISSN: 2186-1390 (Online) http://CenNSER.org/IJCVSP Abstract In recent years, there has been a fairly rapid increase in the number of melanoma skin cancer patients. Melanoma, this deadliest form of skin cancer, must be diagnosed early for effective treatment. So, it is necessary to develop a computer-aided diagnostic system to facilitate its early detection. In this paper, the proposed work is based on a combination of a segmentation method and an analytical method and aims to improve these two methods in order to develop an interface that can assist dermatologists in the diagnostic phase. As a first step, a sequence of preprocessing is implemented to remove noise and unwanted structures from the image. Then, an automatic segmentation approach locates the skin lesion. The next step is feature extraction followed by the ABCD rule to make the diagnosis through the calculation of the TDV score. In this research, three diagnosis are used which are melanoma, suspicious, and benign skin le...
Detection of skin cancer " Melanoma " through Computer Vision
— In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks to perform a classification of the different kinds of moles. As a result, this algorithm developed after an analysis of 200 images was obtained a performance of 97.51%.
IEEE Journal of Selected Topics in Signal Processing, 2000
In this paper, we describe an automatic system for inspection of pigmented skin lesions and melanoma diagnosis, which supports images of skin lesions acquired using a conventional (consumer level) digital camera. More importantly, our system includes a decision support component, which combines the outcome of the image classification with context knowledge such as skin type, age, gender, and affected body part. This allows the estimation of the personal risk of melanoma, so as to add confidence to the classification. We found that our system classified images with an accuracy of 86%, with a sensitivity of 94%, and specificity of 68%. The addition of context knowledge was indeed able to point to images that were erroneously classified as benign, albeit not to all of them.