Increased machine learning methods have helped improvise human interaction with digital devices which helps in skin disease identification, prediction, and classification by employing algorithms. Image classification for skin disease application algorithms can detect caucasian skin tones but poorly performs when analyzing other skin colors. In this research, a deep learning algorithm was used to address the problem that other applications perform poorly with the classification of skin disease types.
Convolutional Neural Network (CNN), a machine-learning algorithm was used to classify images and add the predicted images within the data set. The images in the data set covered a lot of patient factors such as age, sex, disease site (hand, feet, head, nails, etc.), skin color (white, yellow, brown, black) and different periods of lesions (early, middle, or late). Multiple private applications can detect skin diseases during the analysis. For the darker color skin population, the performance was poor, and skin cancer detection was not possible even with the help of image recognition.
This research aims to conduct an analysis of visual searches within skin-related health searches to identify opportunities to provide digital health consumers with visual search results that are more representative of America’s diverse populations.