Spectral indexes obtained by implementation of the fractional Fourier and Hermite transform for the diagnosis of malignant melanoma

Abstract

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.

Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features

“…Because melanoma is deadly cancer, most of those works are focused on identifying it. Few works present systems that classify more than two types of skin lesions [40][41][42][43][44][45], like Wu et al [42], that use five convolutional neural networks to classify face skin lesions of the Xiangya-Derm database. They selected 2656 face images of seborrheic keratosis, actinic keratosis, rosacea, lupus erythematosus, basal cell carcinoma, and squamous cell carcinoma.…”

Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network

“…According to the literature review, there are some proposals for multi-class skin lesion classifications using an ANN, signatures via spectral densities, fractional Fourier transform, Hermite transform, statistical information and Asymmetry, Border inconsistency, Color variety, and Diameter metrics (i.e., ABCD metrics). However, most of them consider a reduced class amount and do not consider the spectral information on an additive color model as input to an ANN [26]- [28].…”