# Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
## 1 Introduction
This variability negatively affects the precision of detection results [10; 13]. Some analyses indicate that color constancy significantly increases the accuracy in diagnosing malignant lesions in images [4]. In this regard, a comparative evaluation between approaches that use local and global characteristics demonstrated the superiority of color characteristics over textural ones in identifying melanomas. Among the evaluated strategies, the approach focusing on local characteristics proved more effective, underscoring the importance of color adjustments to increase the accuracy of automatic diagnosis [14]. This evidence suggests that lighting correction techniques generate positive effects in maintaining color constancy [10]. [PERSON] et al. [15] provides a comprehensive overview of feature extraction, focusing on four classes: manual, dictionary-based, deep learning, and clinically inspired. This study highlights the evolution of techniques and improvements in the accuracy of detecting malignant skin cancer. The transition from manual techniques to advanced deep learning methods is relevant. However, this study also points out challenges such as the low quality of images in databases described in the literature and the efficiency of generalization across different skin types and lighting conditions, corroborating with [16]. Color standardization plays a significant role in the segmentation of skin lesion [17; 18]. Statistical techniques such as Gray World (GW) [19], Shades of Gray (SoG) [20], and maxRGB [21] stand out among the common approaches to adjust this aspect. These methods seek to uniformly correct the lighting of images through adjustments based on statistical models, which can directly impact the uniformity of colors [16]. However, they may result in the loss of important information for the accurate analysis of skin cancer [22]. New approaches for correcting lighting and color aspects based on deep learning have emerged in the literature. These methodologies can predict and optimize these characteristics in digital images, gaining prominence after multiple validations on public image databases [23]. A significant example of this innovation is the Illumination Equalization using the Counter Exponential Transform (IECET) method, developed by [22]. This method applies counter-exponential transformations to the luminosity component in the HSV color space while maintaining the hue and saturation components. It also uses a regressor based on the ResNet architecture to calibrate the parameters, ensuring the homogeneity and adequacy of lighting in images of skin lesions. The deep learning technique generative adversarial network (GAN) has been applied in new color normalization processes for medical images, being adaptable to identify color constancy through uniform lighting adjustment. This methodology benefits from both supervised and unsupervised learning. A notable method is the Dermo Color Constancy-Generative Adversarial Network (DermoCC-GAN), developed by [16], which manages to heuristically preprocess images of skin lesions, significantly improving lighting and color constancy. Dermatologists have positively evaluated the correction results achieved by this method [13]. Lighting calibration, aimed at improving accuracy in color representation, is already a common practice in histopathological images [24]. Applying deep learning architectures has proven promising in correcting lighting and color aspects, and improving tumor and cancer detection [25]. [PERSON] et al. [26] proposed a notable study employing GAN with a generator network of dual decoders to ensure consistency between outputs. Furthermore, it also utilizes histogram losses. The technique adapts to color variations while keeping the structural information intact. The results of this study demonstrate an improvement in classification performance. While statistical methods can improve the quality of images, they face significant limitations for skin lesion images [26].