خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

Abstract

For quick diagnosis, treatment, and healing, many diseases need to be caught early. Delays in diagnosis can lead to other risks. Recently, researchers have been using artificial intelligence to find many diseases quickly and accurately. In particular, they have been using machine learning, CNN, and optimisation algorithms to pick the right features for a simple training model for the classification stage. As most data sets have noisy and repetitive features in all application areas, this slows down the performance of the classifier and may even make the classification less accurate because the search space is so big. This also affects the runtime of the classification. This review gives a full look at Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), and Grey Wolf Optimisation (GWO). It will also talk about how these methods can be used to diagnose diseases like skin cancer, adrenal gland tumours, diabetes, coronary heart disease, and others. Also, the different procedures that researchers have taken to improve the accuracy and speed of diagnosis, the changes they have made to these algorithms, hybrids of these algorithms, and proposed future trends in every search The base of this study is to help new researchers get an overview of swarm intelligence algorithms and their role in diagnosing diseases and to brighten their horizons for the future directions in this field.