# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 1 Introduction  
The skin is the largest organ in the human body and serves a vital function in protecting the body from infection, heat, and ultraviolet radiation. However, the most significant threat to human life is cancer. There are numerous types of cancers that may manifest in the human body, with skin cancer being one of the most lethal and rapidly growing tumours. In fact, one in every three cancers that develop in the human body is skin cancer [1]. In recent years, there has been an increase in the incidence of various dermatological conditions, as well as an increase in the probability of malignant changes associated with these conditions. The consequences can be difficult to control if not diagnosed and treated in a timely manner. Skin cancer screening represents the primary method of reducing the incidence of skin cancer. However, many individuals lack awareness about skin cancer screening and cancer in general. Dermatologists diagnose skin cancer with the naked eye with approximately 60% accuracy. With the use of a dermatoscope and proper training, this accuracy can be increased to 75% to 84% [2, 3]. Pathologists require over a decade of diagnostic experience, yet in some remote areas, the diagnostic proficiency of doctors is often limited [4]. A significant proportion of skin cancers originate in the upper layers of the skin. The development of skin cancer is characterised by the uncontrolled proliferation of skin cells. The normal process of skin cell turnover involves the death of old cells and the production of new cells. However, when this process is disrupted, the cells undergo rapid and disorganised growth, forming tumours [5, 6]. The aetiology of this condition is multifactorial, with a multitude of potential causes, including alcohol consumption, tobacco use, allergic reactions, viral infections, environmental alterations, and ultraviolet (UV) radiation exposure. There are seven common dermatological conditions, namely actinic keratosis (AKIEC), basal cell carcinoma (BCC), benign keratosis (BK), dermatofibroma (DF), melanoma (Mel), nevus melanocyticus (NV), and vascular lesions (VASCs) [7]. The primary method of diagnosis for dermatological conditions is through dermoscopic techniques. The research methodologies employed in the analysis of dermoscopic images have evolved over time, progressing from an exclusively dermatological-expertise-based approach to the utilisation of traditional machine learning algorithms and, more recently, the integration of neural networks [8]. Dermoscopy is a non-invasive microscopic technique that enables visualisation of the subtle structures of, colour changes in, and pigment networks of the skin surface that are difficult to discern with the naked eye [9]. It allows a physician to magnify the area of pigmented skin lesions with a magnifying glass to 6 to 40 times magnification. In comparison to the observation of lesions with the naked eye and the use of common photographic techniques, the presentation of details in dermoscopic images facilitates the determination of the correlation between lesions and the underlying skin histopathology. This enables specialised physicians to analyse lesion characteristics for the diagnosis of skin cancer in a rapid and accurate manner in accordance with established diagnostic criteria, such as the ABCD guidelines, the Monsignor's scoring method, and the seven-point detection method [10]. Skin cancer can be treated effectively when it is diagnosed at an early stage and excised promptly. However, the early stages of skin cancer do not exhibit any discernible abnormalities on the body, and the areas of melanin deposition are highly similar to benign moles. This necessitates a remarkably high level of expertise on the part of dermatologists. Even when dermoscopic images are analysed, dermatologists may have disparate observations of skin cancer due to differences in their skill levels and clinical treatment experience and may be unable to accurately determine whether skin cancer is present [11].