Skin disease detection using deep learning

A complex topic such as dermatology makes it one of the most unexpected and challenging professions to diagnose due to the complexities of the subject matter involved. According to dermatology, it is a regular practice to do extensive tests on patients to ascertain the kind of skin illness they have been afflicted duration of time varies from one practitioner to the next, depending on their experience. It is also influenced by the individual's personal experience with the subject matter. Using a technique not restricted by these limits is essential to diagnose skin diseases without these limitations. This work provides an automated image-based method for diagnosing and categorizing skin problems that use machine learning classification. Computational approaches will be used to analyze, process, and relegate picture data to consider the many different characteristics of the photos that are being processed. Skin photographs are first filtered to remove undesirable noise from the image and then processed to enhance the picture's overall quality. It is possible to extract features from an image using advanced techniques such as Convolutional Neural Network (CNN), classify the picture using the softmax classifier's algorithm, and provide a diagnostic report as an output. With more accuracy and faster delivery of results than the previous technique, this application will be a more efficient and reliable system for dermatological illness diagnosis than the conventional method. Furthermore, this may be a reliable real-time teaching tool for medical students enrolled in the dermatology stream at a university studying dermatology.

Automated summary

Dermatology is a complex field that requires extensive testing to diagnose skin diseases. This research provides an automated image-based method for diagnosing and categorizing skin problems using machine learning classification. This application offers more accurate and faster results than the traditional method, and may also be used as a real-time teaching tool.