تصنيف الصور الطبية باستخدام الذكاء الاصطناعي : دراسة

Abstract EN

In recent years, the use of computing has increased along with medical skills, and this had impressive results in terms of classification and treatment, in addition to facilitating the matter of medical personnel.

This was evident during the Corona pandemic, which infected millions around the world, which led to an almost complete closure to avoid the spread of the disease more and more.

There is an urgent need for software tools to help classify the disease without the need to resort to doctors.

The matter is not limited to the classification of corona disease, but it also extends to the expansion of the discovery of other diseases such as malaria, skin cancer and other diseases that afflict large numbers of people.

Malaria is an infectious disease caused by the Plasmodium parasite, and according to some statistics from the World Health Organization, the total number of infections in 2019 reached about 228 million cases around the world.

As for skin cancer, it is considered one of the serious diseases that affect humans because the skin plays a key role in protecting muscles and bones, and therefore cancer will affect all functions of the body.

Convolutional neural networks have made great strides in many intractable problems in image processing and classification, but their performance depends on their chosen hyperparameters which is a tedious task if done manually.

Therefore, experts in the field of deep learning aspire to improve its performance sometimes by integrating it with other algorithms such as (PSO) Particle Swarm Optimization, (GWO) Gray Wolf Optimization, Genetic Algorithm GA or firefly.

All of these algorithms gave different results than the others, that is, they gave different levels of performance The aim of this research is to find a deep learning model that gives the best accuracy when classifying and we rely on Accuracy as a metric for evaluating the model.