MEDICAL IMAGES CLASSIFICATION USING DEEP LEARNING WITH XCEPTION MODEL

Abstract

In the domain of medical image classification, the Xception model stands out for its advanced performance in analyzing intricate image data. This research applies the Xception model to classify chest computed tomography (CT) images into four distinct categories: adenocarcinoma, large cell carcinoma, normal, and squamous cell carcinoma. Xception, renowned for its use of depthwise separable convolutions, enhances feature extraction by effectively capturing complex patterns with reduced computational cost. The study encompasses a rigorous evaluation of the Xception model through extensive training, validation, and testing phases on a specialized multi-class chest CT image dataset. The dataset includes a balanced representation of the four classes to ensure robust model performance across varying conditions. Key aspects of the evaluation include the model's accuracy in distinguishing between different types of carcinoma and normal tissues, as well as its efficiency in handling computational demands. The results demonstrate that the Xception model provides superior classification accuracy and reliable diagnostic performance. By leveraging its advanced architecture, the approach significantly improves the precision of medical image classification, offering valuable insights for enhanced diagnostic support in clinical settings. This work underscores the effectiveness of the Xception model in advancing medical imaging analysis and its potential impact on improving patient care through more accurate disease classification.