Improving Medical Image Segmentation Using Machine Learning: The Role of Optimization of Extracted Features in Convolutional Neural Networks

Subject Areas : New technologies in distributed systems and algorithmic computing

Moslem Kaviani ¹ , Umm Kulthum Shahriari ^{2 *}

1 - Department of Computer Science, Faculty of Engineering, Azad University, Sanandaj, Iran
2 - Department of Computer Science, Faculty of Engineering, Azad University, Sanandaj, Iran

Received: 2025-03-15 Accepted : 2025-05-28 Published : 2025-06-07

Keywords: Machine learning, Medical image segmentation, Federated learning, Deep segmentation architecture with feature compression,

Abstract :

In recent years, deep learning-based convolutional neural networks have made significant progress, especially in the field of medical image segmentation. However, factors such as uniform layer design, insufficient utilization of multi-scale information, and increased model complexity due to the large number of parameters have limited the performance of these methods in complex situations. In addition, data security and privacy are major challenges in medical data processing. In this study, an improved convolutional encoder-decoder model is introduced that uses strategies to optimize the extracted features and reduce the number of parameters. By employing basic information preservation mechanisms and dense attention modules, this model improves the ability to extract multi-level information and offers greater security and efficiency as an optimal option for use in a federated learning structure. Evaluation of the proposed model on the ClinicDB-CVC dataset shows that this method provides better performance in terms of intersection mean criteria on alliance and Dice coefficient compared to other state-of-the-art methods.

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