## 1 Introduction  
Improving and ensuring healthy lives and promoting wellbeing in the community are some of the United Nations' Sustainable Development Goals. For that reason, empowering and strengthening health practitioners with sufficient technological advancement (to attain the maximum potential of their health practices) is mandatory. Among other kinds of diseases, skin diseases have increased dramatically in recent decades, and they are now considered a major chronic disease globally [1]. People suffer from a broad spectrum of skin disease types (ranging from low to high severity level), such as eczema, chickenpox, measles, warts, acne, skin tumors, and others [2, 3, 4]. Skin tumors (melanoma, basal cell carcinoma, squamous cell carcinoma, and others) are the most dangerous type of skin disease and may be fatal if not treated early. For example, melanoma is a form of skin tumor that begins in cells known as melanocytes. Melanoma is more dangerous because of its ability to spread to other organs more rapidly if it is not treated early. To avoid complexities in skin tumors, an initial consultation with a healthcare professional is mandatory. Furthermore, health practitioners must also be equipped with a more accurate, robust, and sustainable solution for diagnosing and treating such diseases. Artificial intelligence can reduce the skin disease epidemic by providing on-time assistance or advice toward the appropriate action for the diagnosis and treatment of skin diseases (assisting the dermatologist) [5]. Some recent studies participated in diagnosing skin diseases using machine learning. Hyperspectral imaging, laser doppler flow meter [6], and fluorescence spectroscopy have been used to diagnose skin flow oxidation for early diabetes complications [7]. The influence of blood pulsation is essential when analyzing skin disease, and it can be measured by pulse oximetry and photoplethysmography measurements [8]. One study [9] proposed applying mechanical pressure (using a fiber-optic probe) on the diffuse reflectance spectra of human skin measured in vivo [10]. A recent study presented a snapshot from a multi-wavelength imaging device for in vivo skin diagnostics [11]. In comparison, laser doppler flowmetry and skin thermometry are also essential for functional diagnosis [12]. Some efforts also have been made to diagnose skin disease using high-dimensional imaging data. One study [13] proposed a compact hyperspectral-based analysis system to visualize skin chromophores and blood oxygenation. Furthermore, polarized hyperspectral imaging and machine learning represent a practical approach to diagnosing skin complications of diabetes mellitus at a very early stage [14]. Another study [15] proposed a transfer learning-based deep convolutional neural network (CNN) to classify skin diseases. The proposed solution is continuously fine-tuned through a weight upgradation approach (using backpropagation) to universally classify skin disease. However, the obtained classification accuracy was not desirable and simulated scenarios could not be applied to clinical trials. The two significant issues primarily reported when identifying skin disease (using machine learning) through dermatological images are data collection [16] and image transformation and feature extraction. Dermatologists are underrepresented in publications describing these technologies. Increased involvement and leadership of dermatologists are paramount to designing clinically relevant and efficacious models [17]. Notably, in clinical trials, dermoscopy, microscopy, or biopsy [18] devices have been used to obtain skin disease for analysis purposes. For example, an early study [19] used image segmentation and feature extraction to diagnose skin diseases. Another study [20] proposed an artificial neural network (ANN) based skin lesion classification model to classify the lesion into melanoma, abnormal, and typical classes. Moreover, [21] proposed a melanoma detection system using multiscale lesion-biased representation (MLR) and joint reverse classification (JRC).