A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

• Published in: International journal of intelligent systems 2024-05, Vol.2024, p.1-10
• Main Authors: Xing, Kongduo, Ku, Junhua, Zhao, Jie
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Computer vision; Datasets; Deep learning; Digital imaging; Image processing; Image segmentation; Machine learning; Medical imaging; Medical research; Neural networks; Object recognition; Optimization; Semantics; Surveillance; Video
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1155/2024/4337255

To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.