Diagnosing skin disease using deep features based on artificial intelligence
Subject Areas : International Journal of Smart Electrical Engineering
Hassan
Masoumi
1
(Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran)
Fatemeh
Mosalanejad
2
(Department of Medical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran)
Mehdi
Taghizadeh
3
(Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran)
Mohammad
Ghanbarian
4
(Department of Medical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran)
Keywords: Artificial Intelligence, deep learning, convolutional neural network (CNN), skin disease psoriasis and eczema,
Abstract :
Misdiagnosis of skin diseases is a common occurrence. Psoriasis is a skin disease that has many similarities with other diseases, and its incorrect diagnosis causes many problems in the treatment process. Misdiagnosis of this disease causes doctors to face problems during treatment. The lack of images of the disease and the database of skin diseases reduces the diagnosis and the coordination of diagnostic methods, therefore, diagnosis using different images is very useful. Today, diagnosis methods using deep features in medical images have received much attention. Artificial intelligence is one of the automatic methods of diagnosis. These methods can detect new data entering the system and keep it in memory. Therefore, in this article, two different groups of data have been identified using deep features based on artificial intelligence. In this method, the data of the first group in the form of training and testing and the data of the second group are studied gradually. If they are correctly identified, the next 0.1 chunks of data enter the network without testing. If they are wrongly recognized, they enter the training section and this reduces the training process. In this work, by training 20% of the data, i.e. the first 10% and the fourth 10%, there was no need for training because the accuracy was not less than98%. In this article, deep features of images were first extracted using convolutional neural network, and then psoriasis and eczema were diagnosed with average accuracy of98.3%and sensitivity of 97.9% in skin images using artificial intelligence.
