Analysis Of CNN Algorithm In Deep Learning-Based Medical Image Classification

Abstract

The advancement of artificial intelligence technologies, particularly in the field of deep learning, has driven the application of Convolutional Neural Network (CNN) algorithms in various domains, including medical image classification. This study aims to analyze the performance of CNN in classifying medical images associated with different diseases using a standard CNN architecture. The dataset utilized consists of labeled X-ray and MRI images based on medical diagnoses. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess how effectively the model recognizes complex visual patterns. The results demonstrate that CNN achieves high accuracy in identifying objects within medical images, with an average F1-score exceeding 90% on selected datasets. These findings suggest that CNN has significant potential to support automated and efficient medical diagnosis, although further clinical validation is necessary for real-world implementation.