BASIC DISEASE DETECTION BASED ON NEURAL NETWORKS

Аннотация

В статье рассматриваются основы и практические приложения обнаружения заболеваний на основе нейронных сетей. В статье приводится подробная информация об основных типах нейронных сетей, в частности, сетях прямого распространения, сверточных сетях (CNN), рекуррентных сетях (RNN) и глубоких нейронных сетях (DNN). Также анализируется применение этих сетей в таких областях, как онкология, кардиология, неврология, эндокринология и инфекционные заболевания, их эффективность и преимущества в обнаружении заболеваний. В статье также рассматривается широкое использование нейронных сетей в медицине, а также ограничения, проблемы безопасности и понятности данных. Выпуск журнала №-26 Часть–6_ Май –2025 48 MODERN EDUCATION AND DEVELOPMENT Ключевые слова: Нейронные сети, болезнь, федеративные сети, сверточные сети (CNN), рекуррентные сети (RNN) и глубокие нейронные сети (DNN), онкология, кардиология, неврология, эндокринология, инфекционные заболевания, безопасность данных, проблемы понятности. Neural networks, one of the most advanced branches of artificial intelligence, provide great opportunities for early detection of diseases and effective organization of diagnostic processes in the medical field. The need for scientific research and the introduction of new technologies to improve the role and capabilities of neural networks in medicine is emphasized. Neural networks are artificial systems that have the ability to self-learn and are capable of generating complex representations from large amounts of data. These networks consist of many "neurons" (or nodes) and their interconnections, and are used to process and analyze data. In modern medicine, early and accurate disease detection is essential to improve patient health, make treatment effective, and reduce mortality. However, traditional medical methods, such as visual examinations and laboratory tests, can sometimes be time-consuming and error-prone. Therefore, artificial intelligence (AI) and machine learning technologies, especially neural networks, are emerging as effective tools for disease detection. Neural networks are computer systems that work in a manner similar to the human brain, and they are widely used in medicine to analyze, detect, and predict data. In disease detection, neural networks are usually divided into the following main types. Feedforward Neural Networks. These networks pass data from the previous layer to the next layer, and the nodes in each layer are interconnected. They are used to perform simple tasks in disease detection, such as assessing the risk of disease based on patient data. The advantage of using these networks is that they are simple and efficient, but they are only useful for analyzing specific and simplified cases. Выпуск журнала №-26 Часть–6_ Май –2025 49 MODERN EDUCATION AND DEVELOPMENT Convolutional Neural Networks (CNN). CNNs are mainly used in analyzing medical images. For example, medical images such as X-rays, MRIs (magnetic resonance imaging), and CT scans detect diseases, including tumors and nodules. These networks help to automatically identify and classify features from images. CNNs can be used to. Cancer detection: For example, in mammography or early detection of skin cancer. Breast and lung tumor detection: Using computed tomography (CT) or X- ray images. Recurrent Neural Networks (RNNs). RNNs are effective at analyzing sequential data and are used to detect diseases that change over time. For example, they are used to predict heart disease by analyzing a patient's daily status or heart rate changes over time. RNNs have the ability to learn from data that changes over time, which is important when monitoring patients' vital signs and analyzing changes. Deep Neural Networks (DNN). These networks, which are based on deep learning, have many layers and perform complex and high-precision analyses. DNNs are used to detect complex systems, for example, to identify unknown diseases or to analyze large data sets. They are used to analyze medical images or genomic data, for example, to detect cancer and other serious diseases. Application of Neural Networks in Disease Detection. Neural networks are used in medicine to detect and diagnose various diseases. Technologies based on neural networks are widely used in the following areas. Oncology. Neural networks are used to analyze images and detect various tumors and cancers. For example, by analyzing mammography images or skin images, diseases such as skin cancer can be detected at an early stage. CNNs are effective in detecting breast tumors, the presence of nodules in the lungs, and other tumors. Cardiology.Neural networks, especially RNNs, are used in the prediction of heart disease. They can detect heart disease early by analyzing a patient's electrocardiogram (ECG) or heart rate. RNNs are effective in analyzing heart rhythm and other biometric data, taking into account changes over time. Neurology. Neural networks can also help in detecting Parkinson's, Alzheimer's, a