Harnessing CNNs for Accurate Skin Disease Identification

Skin disorders are common and can be brought on by several things, including viruses, bacteria, allergies, or fungi. The speed and precision of detecting skin diseases have increased because of developments in laser and photonics-based medical technologies. The expense of such a diagnostic is still considerable, though, which restricts accessibility. An automated early-stage dermatological screening system has been developed using image processing techniques to solve this issue. Effective skin disease classification requires the extraction of key information from pictures, and many detection algorithms rely on computer vision. DenseNet169 and NASNetMobile are some of the methods that were employed in the study. Graphs were used to display the results as the researchers compared the algorithms' performance based on training accuracy and confusion matrix. Keywords: Skin Disease, Medical Diagnosis, Deep Learning, Transfer Learning, CNNs