# Skin Cancer Disease Detection Using Transfer Learning Technique
## 1 Introduction  
The unchecked increase in irregular skin cells that leads to malignant tumors is skin cancer. Most of these malignancies are caused by unprotected skin exposure to ultraviolet (UV) radiation [1; 2; 3]. Melanomas account for 1% of all skin malignancies, with the other 99% being basal cell carcinoma or squamous cell carcinoma [4]. It is one of the most common diseases in American society and a serious one. In the United States alone, more than five million different cases of skin illness are reported every year [5]. For decades, skin cancer has been progressively rising [6]. Melanoma has become the most severe skin cancer and is responsible for around 75% of all skin cancer mortality [7]. The American Cancer Society reported that over 99,780 new cases of melanoma would be discovered in 2022, whereas about 57,100 cases will be reported in men and 42,600 in women. It is expected that about 7650 people will die from melanoma [4]. Melanoma affects the melanocytes (squamous cell layer). Based on cancerous cell severity, it may be further divided into benign and malignant categories. A benign skin lesion is a mole or tag that does not contain cancerous cells. Malignant lesions necessitate immediate treatment due to a high concentration of cancer cells [8]. According to current figures, the survival rate is 99% if the melanoma is detected before spreading near lymph nodes [4]. The survival rate is about 68% after melanoma spreads near lymph nodes, and the survival rate is about 30% in case the melanoma spreads near lymph nodes and other organs [4]. The statistics show that in2019 about 1,361,282 people were living with melanoma [9]. In 2020, about 324,635 people were diagnosed as melanoma patients, and about 57,043 died from melanoma [10]. Doctors use a variety of ways to detect skin cancer. An expert dermatologist usually follows a series of benchmarks, starting with naked-eye recognition of suspicious tumors, then dermoscopy, and finally a biopsy [11; 12]. It can take a long time, and the person may advance to a later step. The detection performance of dermoscopic images has increased by 50%, with absolute accuracy ranging from 75% to 84% [13]. Furthermore, correct diagnosis is unique and highly dependent on the clinician's abilities [14]. The manual identification of skin diseases is very tough and tiring for patients [15]. Because computer-assisted diagnosis helps the medical experts in analyzing the dermoscopy procedures in case of a lack of expertise in the diagnostic process and lack of availability of a professional [16; 17]. A computer-based classification is an option for diminishing inter- and intra-variability. The state-of-the-art computer-assisted dermatological image categorization systems had two fundamental flaws; there are inadequate data [18], and the imaging process is the second difficult challenge, in which skin images are obtained using a specific instrument called dermoscopy [19], whereas other medical images, such as biopsy images and histology images, are obtained using biopsy and microscope. The state-of-the-art approaches [20] needed substantial preprocessing, segmentation, and feature extraction operations to categorize skin images. Artificial intelligence is a novel area; the revolution related to it is similar to that made by adding techniques to every part of our lives [21; 22; 23]. Machine learning (ML) methods assist in avoiding the step of manually extracting features and help perform classification tasks [24] efficiently. Recently, there has been growing attention in employing ML approaches to help accurate cancer detection [24; 25]. Machine learning algorithms have significantly increased cancer prediction accuracy by 15% to 20% during the last few decades [25]. Deep learning [26; 27; 28; 29; 30] is one of AI's most rapidly growing topics due to its broad range of applications.