GastroFPN: Advanced Deep Segmentation Model for Gastrointestinal Disease with Enhanced Feature Pyramid Network Decoder.

The early identification of gastric cancer holds considerable importance within medicine due to its crucial role in mitigating fatality rates. Currently, artificial identification and annotations using gastroscopic images are the main methods of evaluation. Nevertheless, doctors have significant difficulties implementing these techniques due to the considerable heterogeneity in the visual characteristics of early cancer tumors. Weakness in segmentation remains the biggest obstacle to accurate detection and extraction of the main lesion from the tumor. In this paper, we propose a deep model combining two networks encoded: Unet++ and the feature pyramid network. The encoder backbone on first detection is based on ResNet34, which feeds the feature extraction to the next step. The second step is adding an enhanced feature pyramid network by merging blocks and final segmentation heads. The decoder improves the model's capacity to collect hierarchical characteristics at many levels, resulting in enhanced segmentation performance that adapts to changes in illness symptoms. The proposed model achieved a segmentation accuracy of 96.8%, a dice-score of 86.6%, and an F1-score of 85.3% when using the EDD2020 dataset. While the accuracy for DCSA-Unet achieved 92%, Unet++ 90%, 77% for FPN, and DeepLabv3+ 94.2%, We trained the proposed model on two different datasets, the CVC-ClinicDB and Kvasir-Seg datasets. For CVC-ClinicDB, the results metrics registered a Dice-Score 91.64%, an IoU of 84.63%, and an accuracy of 98.55%. For the Kvasir-Seg dataset, the Dice score is 92.54%, the IoU is 87.57%, and the accuracy is 96.62%. [ABSTRACT FROM AUTHOR]