Abstract:
A widely recognized paradigm exists for imaging purposes in the identification of skin disorders.
In recent years, many medical professionals, including physicians, use CAD to assist them more
accurately detect a range of disorders by looking at clinical pictures. Skin cancer is one of the most
deadly diseases in the world. It's challenging to diagnose skin cancer correctly, though. This
research offers several image processing techniques to help diagnose different kinds of skin
diseases. The purpose for this research is to ascertain whether or not both these eights’ skins are
diseases, as well as to identify the type of skin illness by utilizing a variety of skin disease classes.
In addition to eight additional class types, deep learning-based methods were employed in this
experiment: 'BA-cellulitis', 'BA-impetigo', 'FU-athlete-foot', 'FU-nail-fungus', 'FU-ringworm',
'PA-cutaneous-larva-migrans', 'VI-chickenpox', and 'VI-shingles'. Five different models are used:
DenseNet169, InceptionV3, VGG16, VGG19, and InceptionResNetV2 to forecast and detect skin
photos and categorize illnesses. Lastly, two distinct efficiency metrics are used to evaluate the
approach's performance. Five possible results are used in the initial accuracy set, which rates
performance under both normal and fractured circumstances: TP, TN, FP, and FN. These models
are then applied to investigate the exact nature of each sort of illnesses in mistake settings. With
an accuracy percentage of 98.23%, the InceptionResNetV2 technology enables my suggested
method to independently identify different kinds of skin disorders. In the end, data classification
using the InceptionResNetV2 networks to identify skin illnesses results in the creation of an
application in web.
