A YOLO-Powered Deep Learning Approach to Psoriasis Classification - Goswami, Anushree; Sharma, Nidhi (International Journal of Computer Sciences and Engineering, 2024)
With the rise of technological advancements, various clinical practices have undergone significant transformations. The field of dermatology, in particular, has experienced rapid progress. Skin ailments encompass a spectrum of conditions impacting the human body`s largest organ. These conditions range in severity from mild instances like acne or eczema to more serious cases such as skin cancer or Psoriasis, which is a persistent inflammatory skin disorder affecting a considerable global population. The precise categorization and assessment of the severity of Psoriasis play a pivotal role in its effective treatment and management. Conventional classification methodologies often prove subjective, time-intensive, and susceptible to variations in interpretation amongst different observers. In contrast, machine learning and deep learning, subsets of artificial intelligence, are revolutionizing various domains by addressing diverse challenges autonomously, without the need for human intervention. AI technologies have opened up fresh avenues for the objective and automated classification of Psoriasis. However, these technologies are yet to attain their maximum potential in terms of accuracy. Here, we try to implement a relatively new method i.e., YOLO (You Only Look Once) which is basically an object detection technique, to try to classify psoriasis. A comparison of all the different models of YOLOv8 have been studied here. The study also deploys the Google Colab platform for computational needs and ease.