# Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
## 1 Introduction  
Tropical countries exhibit a high incidence of malignant skin cancer, with carcinoma and melanoma being the most common types [1]. These neoplasms have the potential to develop throughout the body, with a higher likelihood in areas most exposed to the sun [2]. Risk factors include fair skin, family history, and the presence of atypical lesions. Melanoma is the most aggressive type of skin cancer due to its metastatic capacity, but carcinomas can also reach this stage [3]. Color is an essential aspect in the diagnosis of malignant lesions. Variations in the hues of a lesion can reflect abnormalities, aiding in the diagnosis of skin cancer [4]. Shades of brown, black, or other atypical colors may indicate the presence of malignancies [5]. Early identification of changes in lesion color is an important factor for early diagnosis and effective treatment [6]. The diagnosis of skin cancer is fast; the problem lies in the delayed search for medical care by patients [7]. In early stages, malignant lesions can be confused with benign lesions due to the lack of clear symptoms. An increasingly adopted solution in dermatology is the visual monitoring of lesions, aided by computerized systems for analysis of digital images [8]. The use of deep learning has shown promising results comparable to the diagnostic accuracy of dermatologists [9]. The quality of the images is a relevant feature in developing deep neural network models and requires proper treatment [10]. Public image databases training these networksdo not follow a standard capture protocol regarding lighting [11]. Another factor is the variability of sensors, such as dermatoscopes, digital cameras, and smartphones [12]. The lack of standardization can compromise the consistency in the representation of colors and the reliability in the automated classification of neoplasms in digital images [5]. Considering the importance of accurate diagnosis and the challenges posed by variations in lighting and color in skin images, we propose a study that investigates the relevance of these aspects in the classification of skin cancer with deep learning. The goal is to understand the impact of lighting and color variations on classification models employing deep neural networks. This research develops an approach that combines existing techniques but prioritizes innovations in correcting lighting and color variations and the correlations of these variations in accurate classification with deep learning models. The justification for investigating this work lies in the advances in deep neural networks. The progress of these techniques has allowed for increased accuracy in detecting malignant lesions in digital images. Therefore, it is essential to investigate the need to propose such techniques for the correction and treatment of lighting and color variations in images of skin lesions. Furthermore, in addition to proposing corrections for such variations, it is also necessary to understand how the interaction between lighting and color in classification with deep learning affects the precise identification of malignant lesions. The main contribution of this research is to develop an approach that synergistically combines techniques based on the clinical optics of identifying malignant skin cancer. This approach prioritizes aspects of lighting and color, considering the divergence in the capture and treatment of skin lesion images. Moreover, this approach adapts, applies, and optimizes deep learning techniques never used before to deal with lighting and color bias. Although well-known deep learning models in the literature are used, the contributions lie in the reliable comparative analysis produced by these models. Related WorkThe relevance of color in automated skin cancer diagnosis has been highlighted in several studies, surpassing texture characteristics in the effectiveness of image classification [4; 5]. Variation in lighting directly affects the representation of colors, resulting in a wide range of prototypic appearances in images of malignant lesions.