In real life, images are widely used in medicine, commercial marketing and other fields. Image recognition technology has developed rapidly and has played a huge role in our daily life and work. So in order to make better use of image technology, this paper intends to delve into image content segmentation in convolutional neural networks. This paper mainly studies the role of convolutional neural networks and image segmentation in specific operations through experimental methods and interactive ratio IoU analysis methods. The experimental data show that in the PASCAL VOC 2015 and IRCAD datasets, the optimal parameters obtained by G-FCN are α=0.6, β=0.6. Therefore, the result of using the improved algorithm is close to the marked result, which can better reflect the details of the image, and the segmentation effect is better.

Ernest Dadparvar. Image Content Segmentation Based on Convolutional Neural Network. International Journal of Neural Network (2022), Vol. 3, Issue 3: 36-44. https://doi.org/10.38007/NN.2022.030305.

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