DEVELOPMENT OF AN X-RAY IMAGE RECOGNITION SYSTEM BASED ON DEEP LEARNING

Abstract

The advancement of deep learning methods opens up new possibilities for automated processing and classification of medical images, increasing diagnostic efficiency while reducing the workload on physicians. This study examines the application of convolutional neural networks (CNNs) for the automatic recognition and classification of medical X-ray images using a publicly available dataset from Kaggle. The proposed approach involves sequential data processing, starting with preprocessing and normalization steps and concluding with model training and evaluation on a validation set. 

A deep convolutional neural network architecture was utilized to build the model, comprising multiple convolutional layers with ReLU activation, normalization layers, and pooling layers for dimensionality reduction, as well as a fully connected layer for classification. Training was carried out using the Adam optimizer, which provides rapid and stable minimization of the categorical cross-entropy loss. To address overfitting, regularization methods such as Dropout and Early Stopping were employed, thereby enhancing the model’s generalization capability. 

The model’s effectiveness was evaluated using metrics including accuracy, recall, F1-score, and loss on the test set. Experimental results demonstrated high classification performance, with an accuracy of 92.3%, recall of 91.1%, an F1-score of 91.7%, and a loss value of 0.25. These findings highlight the effectiveness of the proposed approach and its competitiveness with state-of-the-art automated medical imaging diagnosis methods. 

The proposed method can be utilized in clinical practice to assist physicians in disease diagnosis, reducing the likelihood of subjective errors and speeding up the analysis of X-ray images. Moreover, the use of cloud technologies, such as the Google Cloud AI Platform, enables model deployment as a web service for remote access and simplifies integration with medical information systems. Future research will focus on further model refinement through the use of Transformer architectures and the expansion of the dataset to improve generalizability of the results.