This article presents a new methodology to diagnostics ten types of skin lesions based on the image' s Fourier spectral information in an additive color model. All spectral information and correlation coefficients between the skin lesions classes conform the input signals to an Artificial Neural Network. In general, the results show the well-defined classification for all the skin lesions classes based on the high values for Accuracy, Precision, Sensitivity, and Specificity metrics performance and a reduced images misclassification percentage (≈5.9%) for the Testing sub-dataset, and less for Training (≈2.8%) and Validation (≈5.6%) sub-dataset even considering the strange objects, not-clarity, and black sections in some images analyzed. The general achieved classification Accuracy, Precision, Sensitivity, and Specificity percentages of the proposed method are 99.33 %, 94.16 %, 92.9 %, and 99.63 %, respectively. In particular, the skin lesions related to Basal Cell Carcinoma, Seborrhoeic Keratosis, and Melanocytic Nevus present the best performance regarding the Receiver Operating Characteristics, while the Pyogenic Granuloma was the worst classified.INDEX TERMS Artificial neural networks, biomedical computing, image processing, medical diagnostic imaging, Fourier spectral analysis.
In this paper a new methodology for the diagnosing of skin cancer on images of dermatologic spots using image processing is presented. Currently skin cancer is one of the most frequent diseases in humans. This methodology is based on Fourier spectral analysis by using filters such as the classic, inverse and k-law nonlinear. The sample images were obtained by a medical specialist and a new spectral technique is developed to obtain a quantitative measurement of the complex pattern found in cancerous skin spots. Finally a spectral index is calculated to obtain a range of spectral indices defined for skin cancer. Our results show a confidence level of 95.4%.
In this papera methodology for classifying skin cancerin images of dermatologic spots based on spectral analysis using the K-lawFourier non-lineartechnique is presented. The image is segmented and binarized to build the function that contains the interest area. The image is divided into their respective RGB channels to obtain the spectral properties of each channel. The green channel contains more information and therefore this channel is always chosen. This information is point to point multiplied by a binary mask and to this result a Fourier transform is applied written in nonlinear form. If the real part of this spectrum is positive, the spectral density takeunit values, otherwise are zero. Finally the ratio of the sum of the unit values of the spectral density with the sum of values of the binary mask are calculated. This ratio is called spectral index. When the value calculated is in the spectral index range three types of cancer can be detected. Values found out of this range are benign injure.
Noninvasive approaches," J. Am. Acad. Dermatol. 72(6), 929-941 (2015). 12. J. March, M. Hand, A. Truong, and D. Grossman, "Practical application of new technologies for melanoma diagnosis: Part II. Molecular approaches," J. Am. Acad. Dermatol. 72(6), 943-958 (2015). 13. E. Guerra-Rosas and J. Álvarez-Borrego, "Methodology for diagnosing of skin cancer on images of dermatologic spots by spectral analysis," Biomed.
Many people suffer from different skin diseases, which can be diverse and varied. Most skin diseases cause disorders in the skin, such as changes in color, texture, and appearance manifesting in spots, swelling, scaling, ulcers, etc. One of the diseases that represents a serious health problem is skin cancer. The most dangerous skin cancer is malignant melanoma, which can cause death if not detected early. Therefore, development of new and accurate diagnosis methodologies to increase the chance of early detection is important. In this work, an analysis to discriminate between malignant melanoma and three types of benign skin lesions-melanocytic nevus, dermatofibroma, and seborrheic keratosis-is realized by calculating spectral indexes based on the real and imaginary parts of a fractional nonlinear filter obtained by affecting the modulus of the fractional Fourier transform by an exponent k. The fractional spectral indexes were calculated by working with selected sub-images obtained by dividing the input image. Also, a variation was implemented when the Hermite transform is used to calculate the fractional nonlinear filter. Discrimination between malignant melanoma and benign skin lesions was achieved with a 99.7% confidence level.
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