Aiding from Deep Learning Applications in the Classification of Medical Images

One of the important fields in computer science is called computer vision. Computer vision technology can be used to solve many problems in various fields. Computer vision plays a major role in image classification, recognition, and analysis. For example, in healthcare, computer vision algorithms could help with an automatic task, such as detecting cancer in the skin image, classifying breast cancer, and identifying Covid-19 images. Deep learning (DL) is responsible for developing computer vision. Deep learning is a subset of the world of Artificial Intelligence (AI). The aim of this paper is to classify medical images by deep learning algorithms. Several models of deep learning like VGG16, VGG19, ResNet 50, inception v3, etc. are used to improve image classification. Three different data are used in this paper. Data on skin cancer, breast cancer, and Covid-19 were used due to the recent spread of these diseases, as they are among the lethal diseases. The concept of transfer learning is used to reduce the cost of learning and to overcome the problem of overfitting. When models are hybrid with transfer learning, good results appear. Accuracy is 98.03% of classified skin images, 99.35% of Covid, and 97.77% of breast images. These results will be useful to doctors in making medical classification images more accurate.