Advancements in psoriasis classification using custom transfer learning algorithms.

Psoriasis is a skin disorder which mainly occurs as a rash, scaly areas and an itchy skin. The symptoms usually occur on the chest, elbows, and the scalp. World Health Organization (WHO) reports that about 125 million individuals in the world, that is about two to three% of the world population, live with psoriasis. Moreover, approximately 30% of psoriasis patients can have psoriatic arthritis. Psoriasis can have a detrimental effect on the quality of life of the affected people. They can also deal with several chronic diseases, such as depression, metabolic syndrome, cardiovascular diseases, such as atherosclerosis, heart attacks, and strokes. The psoriasis may develop at any age of life, but this disease is most prevalent in the ages between 20 and 30 and 50-60. Psoriasis is caused by different aspects, such as hereditary aspects, lifestyle habits of modern people, diet, use of drugs, skin trauma, stress, or abnormalities in the immune system, and hormonal fluctuations. Regrettably, the available conventional measures of determining the type of psoriasis are not always accurate with human errors. In order to overcome these problems, our study would attempt to develop a new dataset classified into seven classes of psoriasis diseases. We make use of publicly accessible data like SKIN LESION, ISIC and DEMANET. To overcome the problem of class imbalances, we will use the method of image augmentation to make the number of imageries in each class close to each other.To identify the presence of psoriasis in skin disease images, we employ transfer learning algorithms. Specifically, our proposed methodology utilizes ResNet50, InceptionResNetV2, and InceptionV3 with Adam and RMSprop optimizers for psoriasis classification. During training and validation, the ResNet50 model achieved good accuracy rates with values of 92.36, 84.59, and 83.55, respectively. The InceptionV2 model demonstrated impressive accuracy during training, validation, and testing, with values of 99.07, 96.65, and 97.20, respectively. Similarly, the InceptionV3 model achieved superior accuracy rates during training, validation, and testing, with values of 99.57, 96.82, and 98.68, respectively.When compared to all the models, InceptionV3 consistently demonstrates superior accuracy.