Information Technology Usage in Skin Disease Detection

: Millions of individuals of all ages are affected by skin diseases, a widespread problem worldwide. Early diagnosis and detection are essential for these diseases to be effectively treated and improve patient outcomes. Automated skin disease detection systems are a viable way to increase diagnostic accuracy and lighten the workload of dermatologists, by developments in machine learning and computer vision. These systems examine skin lesions and categorize them into several disease groups using various techniques, including feature extraction, deep learning, and image processing. Such systems are still being developed to enhance their precision and usefulness. This paper provides an overview of the different information technologies in skin disease detection, including their effectiveness, the challenges and limitations of existing systems, and future research directions in this field.

: Different model performance on detecting skin disease.
The graph compares the rates of accuracy of various models used to identify particular skin disorders. The ensemble model, which has the best accuracy rate of 85.02% according to the results, suggests that merging different models can increase the precision of skin disease detection [10]. For the purpose of identifying skin problems, machine learning(ML) technology has become widely used in dermatology. Through the use of machine learning methods like fuzzy c means region segmentation and artificial neural networks, numerous studies have proposed remedies for skin disease detection. A new adaptive federated machine learning-based skin disease model that uses an adaptive ensemble convolutional neural network as the primary classifier is described in a paper by Hashmani, Manzoor Ahmed, et al. [11]. This architecture not only has the ability to identify the disease type but also continuously enhances its precision. The International Skin Imaging Collaboration (ISIC) 2019 dataset was used to assess the classification precision and flexibility of the suggested model. The outcomes of this study suggest that the proposed model has the potential to develop a federated machine learning-based dermoscopy device to aid dermatologists in skin tumor diagnosis. For the purpose of identifying skin diseases even if dataset is small, proper preprocessing steps and data augmentation helps deep learning algorithms to identify hidden patterns and easily recognize alopecia, psoriasis, and folliculitis diseases [12]. Moreover, a hybrid strategy combining computer vision and machine learning methods has been developed. The method uses microscopic pictures as input, most specifically histopathological images. Following the extraction of color, shape, and texture features from the images, a convolutional neural network (CNN) is used to classify the images and identify diseases. On the input image, image processing techniques are also used to extract feature values from which the classifier model predicts the disease. In remote locations with limited dermatological access, the suggested system is very helpful. Additionally, the proposed system's tools are open source and free to use, making it possible to deploy the system without spending any money. The system-specific application was made to be lightweight and usable on devices with modest system requirements [13]. Mobile applications have been increasingly used for skin disease detection, providing a convenient and accessible platform for patients to monitor their skin conditions. These apps use various methods to detect skin diseases, such as image recognition technology and artificial intelligence algorithms. A study named "Mobile Applications in Skin Cancer Detection: A Descriptive Analysis" presents a comprehensive analysis of the role of mobile applications in the prevention, treatment, and management of skin cancer. It emphasizes the increasing popularity of dermatology-related apps and their potential to become vital tools in delivering dermatologic care. However, the study also highlights the importance of further research to evaluate the accuracy and reliability of app content and generated diagnoses. The paper further discusses the growing availability of apps accessible to the general public for skin cancer prevention and early detection since 2014 [14]. Raman spectroscopy is a non-destructive analytical technique that uses laser light to measure the vibrational modes of molecules in a sample. It has a history of almost 100 years, and over this period, it has undergone many modifications and developments, including the discovery of lasers, improvements in optical elements, the sensitivity of spectrometers, and the emergence of advanced light detection systems. Raman spectroscopy has many applications in various fields, including chemistry, physics, biology, and materials science. Skin disease identification is one of the potential uses for Raman spectroscopy. Skin research applications for Raman spectroscopy include medication penetration monitoring and analysis, skin composition analysis, and diagnostic dermatological applications. Confocal Raman spectroscopy is one such use. It is a sensitive, non-invasive tool that can study the makeup of the skin and spot changes brought on by illnesses. Additionally, it can be used in cosmetology to determine how different active ingredients and medications penetrate the skin and to what extent and depth [15]. The topic of "Skin Disease Detection Using Image Processing and Soft Computing" is how to identify skin disorders using image processing and soft computing approaches. Digital image analysis is used in image processing techniques to extract features from skin image data and categorize them into several illness groups. To increase the precision of the categorization process, soft computing techniques employ artificial intelligence algorithms, including neural networks, fuzzy logic, and evolutionary algorithms.
The study, which compares various methods for spotting skin diseases, demonstrates that combining methods yields the highest accuracy and improves the functionality of the system. These techniques use cutting-edge technology to improve the early detection and prevention of skin diseases [16]. The proposed method for skin disease recognition described in this paper, titled "Skin Disease Recognition Method Based on Image Color and Texture Features," provides an automated approach to identifying various types of skin diseases. It involves three main steps: preprocessing the skin images to eliminate noise and irrelevant background using filtering and transformation techniques; segmenting the images of skin diseases and extracting accurate texture and color features using the grey-level co-occurrence matrix (GLCM); and using the support vector machine (SVM) classification method to identify herpes, dermatitis, and psoriasis. This method offers a promising solution for accurate and efficient skin disease recognition, which can improve patient outcomes and provide better access to care in areas with limited dermatological resources [17]. Cutting-edge diagnostic methods for detecting skin diseases are now utilizing nanotechnology. Specifically, tiny particles known as Nano Flares are being employed. These Nano Flares are composed of fluorescent molecules and gold and serve as biomarkers that can detect the presence of a disease. The particles are engineered to bind specifically to mRNA molecules associated with the disease being detected. To use this technology, the NanoFlares are applied topically to the skin. They then penetrate the skin and tissue barriers to reach the cells of interest. Once inside the cells, the NanoFlares bind to the target mRNA molecules and emit a fluorescent signal that can be detected using a special imaging technique. Overall, this advanced technology has the potential to significantly improve our ability to diagnose and treat skin diseases [18]. For skin cancer detection artificial neural network includes a series of stages to analyze images of the skin and diagnose whether or not melanoma is present. These stages involve selecting image input, preprocessing the image, improving the quality of the image, segmenting the image, extracting features based on texture, classifying the image using an artificial neural network, and diagnosing the type of skin cancer present [19]  Image Classification suggested method, the paper generates various performance evaluation indicators, including accuracy, precision, recall, and F1 score. The experimental findings demonstrated the viability of the suggested methodology, with MobileNet achieving a classification accuracy of 96.00 and the Xception model achieving a classification accuracy of 97.00 with transfer learning and augmentation. The research demonstrates the efficacy of the suggested models for classifying skin diseases [18]. The proposed skin disease detection method using image processing techniques discussed in the paper "Digital dermatology: Skin disease detection model using image processing" has several advantages, including its mobile-based and non-invasive nature, making it easily accessible for patients in remote areas. The patient only needs to provide an image of the affected skin area as input, and the system performs image processing techniques to detect and display the identified disease. This approach is highly beneficial for areas where access to dermatologists is limited. Moreover, the use of deep learning algorithms improves the decision strategy and overall accuracy of the system. Therefore, this method has the potential to be an effective tool for skin disease detection [21]. The findings of the trials reported in the publication "Skin Disease Recognition Method Based on Image Color and Texture Features" show how effective and useful the suggested disease recognition approach is. Herpes, dermatitis, and psoriasis were effectively diagnosed using this method, which had good accuracy rates. The accuracy of the suggested method was assessed using a variety of performance measures, such as precision, recall, and F1-score. The results show that the proposed method surpassed existing methods in terms of accuracy and efficiency. These results imply that the suggested technique may prove to be a useful tool for the automatic diagnosis of skin conditions [17]. The fact that nanotechnology is non-invasive and self-applicable is one of its main benefits. The need for skin biopsies, which are now the gold standard for identifying skin illnesses, might be greatly decreased as a result of this. Additionally, it may be simpler to identify skin illnesses in off-the-grid or underserved locations with the use of mobile device signal gathering and Internet-enabled transmission [22]. There are several advantages of the artificial neural network, including the ability to detect melanoma skin cancer at an early stage, leading to improved treatment outcomes and potentially saving lives. Additionally, the program enables more precise diagnoses of skin cancer, reducing the incidence of false positives and unnecessary biopsies. This, in turn, leads to increased efficiency in the diagnosis process, saving time and resources for both patients and healthcare providers. Finally, the use of artificial neural networks and image processing techniques has the potential for broader application in other areas of medical diagnosis and healthcare [19].
Dermatologists can detect and classify skin issues more precisely and quickly using the proposed CNN model than they can with the current approach. It extracts features from skin image data using cutting-edge methods like Convolutional Neural Network (CNN), classifies the data using the softmax classifier's algorithm, and outputs a diagnostic report. The model may edit skin images, remove distracting noise, and improve the image's overall quality. The suggested model can also be utilized as an effective realtime teaching tool for medical students at a university who are enrolled in the dermatology stream. Overall, the suggested CNN model can aid in increasing the precision and timeliness of diagnosing skin diseases, which can improve patient outcomes [20].

Ethical Considerations in Using Information Technology for Skin Disease Detection
We must take crucial ethical considerations into account when integrating digital epidemiology into current procedures, including confidentiality, security, accuracy, and fairness. This calls for the confidentiality and protection of individuals' personal health information as well as the provision of secure data transfer and storage. Additionally, we must confirm the accuracy and dependability of the data sources and analysis techniques used. Furthermore, it is critical to employ digital epidemiology tools and data in a fair, just, and discrimination-free manner. Depending on the specific context and use of digital epidemiology, these ethical challenges need careful examination, and potential solutions may differ [23]. This paper provides an ethical analysis of the use of machine learning healthcare applications (ML-HCAs) in dermatology. The authors identify potential benefits and risks associated with the development of dermatological ML-HCAs, such as better patient outcomes and decreased healthcare disparities, as well as confidentiality issues and exacerbation of healthcare disparities. They suggest that ethicists should be involved in the development process of ML-HCAs to ensure ethically and socially responsible development and clinical translation [24]. When using information technology to identify skin diseases, ethical issues must be taken into account. These include problems with patient confidentiality and privacy as well as potential biases in the classification algorithms. Concerns exist around the possible misuse of patient data as well as the requirement for sufficient consent and control over data use. Additionally, it's important to