# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 1 Introduction  
One of the most widespread cancers globally is skin cancer, significantly impacting the quality of human life. The primary cause is excessive exposure of the skin to ultraviolet (UV) light from the sun. Melanocytes, the pigment-producing cells present in the skin's top layer, are the origins of melanoma, a relatively common form of cancer. These cells can undergo malignant mutations due to damage from UV light exposure, both from the sun and tannins beds. However, melanoma can also manifest in areas of the body not typically exposed to sunlight [1]. Nonmelanoma pertains to skin tumors originating in the epidermis, the outermost skin layer, and the affected skin region is often referred to as a lesion. Every year, the global count of reported nonmelanoma skin cancer cases ranges between 2 and 3 million [2] Cutaneous melanoma ranks as the 17 th most prevalent cancer worldwide. Overall, it stands as the 15 th most common cancer, holding the 13 th position among men and the 15 th among women [3]. Skin cells undergo continuous processes of growth and aging on a daily basis. Nevertheless, there are instances where the normal progression of skin growth becomes disrupted, leading to the development of surplus cells, referred to as carcinomas. Among the diverse array of skin cancer types, melanoma emerges as the most malignant and severe variant, significantly contributing to cutaneous mortality rates [4]. Both melanoma and nonmelanomaskin tumors can frequently result in fatalities, as highlighted by [5]. While it holds true that the majority of skin tumors globally are benign and possess a low probability of evolving into skin cancer, it is crucial to recognize the existence of potentially perilous malignant skin tumors. If left untreated, these tumors can spread throughout the body and lead to the demise of the host [6]. Doctors utilize a range of methods for skin cancer detection [7]. Visual assessment serves as an initial approach to gauge the likelihood of the disease. To assist in the preliminary screening of melanoma, the American Center for the Study of Dermatology has developed the ABCD guide, which stands for asymmetry, border, color, and diameter [8]. This guide aids doctors in evaluating the morphology of suspected melanomas. When a potentially concerning skin lesion is identified, a biopsy is conducted on the visible anomaly. Subsequently, the biopsy sample is scrutinized under a microscope to ascertain its benign or malignant nature, as well as to identify the specific type of skin cancer [9]. This methodology faces several challenges, such as the inability to discern lesion characteristics due to hair presence and the resemblance between cancerous and noncancerous lesions. Additionally, certain communities lack access to adequate healthcare resources. In diverse literature, skin cancer classification has incorporated various machine learning techniques, including support vector machines (SVM), neural networks, naive Bayes classifiers, and decision trees. These methodologies have effectively classified distinct skin cancer types. One limitation of machine learning approaches is their dependence on manually engineered features. However, over the past decade, transfer learning methods have gained prominence due to their capacity for automated feature extraction. As a result, they have found widespread application in research studies [10]. The aim of this study is to develop an automated system for the classification of skin lesion images, distinguishing between benign and potentially cancerous cases. Our approach stands apart from similar studies due to the following distinctive features:  
* We employ a transfer learning strategy, utilizing the Densenet architecture, to classify skin cancer as malignant or benign. * Enhancing the Densenet model, we introduce a flatten layer, two dense layers with LeakyReLU activation functions, and an output dense layer with a sigmoid activation function. These modifications aim to boost model accuracy.