<![CDATA[Advances in artificial intelligence technology, particularly in the field of deep learning, have made significant contributions to medical image processing for the early detection of various diseases. This study aims to apply deep learning algorithms, specifically Convolutional Neural Networks (CNNs), in the process of classifying and identifying diseases through the analysis of medical images such as X-rays, MRIs, and CT scans. The use of CNNs enables automatic and efficient feature extraction, thereby improving the accuracy of disease detection compared to conventional methods. The dataset used consists of thousands of medical images that have been manually classified by medical professionals. The model training process was carried out using transfer learning techniques using pre-trained architectures such as VGG16 and ResNet50. Performance evaluation was carried out by measuring the values of accuracy, precision, recall, and F1-score. The results showed that the developed CNN model was able to achieve a detection accuracy level of up to 95.3% on the test dataset. The application of this technology is expected to support computer-based diagnostic systems (CDI) as an aid for medical personnel in clinical decision-making. In addition, this system has the potential to accelerate the screening process and reduce the risk of misdiagnosis. These findings indicate that deep learning technology has enormous potential for improving the quality of healthcare services, particularly in the areas of disease prevention and early detection more effectively and efficiently.]]>
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