| Abstrakt: |
Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop the spread of cancer, early polyp detection is essential. The timely removal of polyps without submitting a sample for histology is made possible by computer-assisted polyp classification. In addition to Locally Shared Features (LSF) and ensemble learning majority voting, this paper introduces a computer-aided decision support system named PolyDSS to assist endoscopists in segmenting and classifying various polyp classes using deep learning models like ResUNet and ResUNet++ and transfer learning models like EfficientNet. The PICCOLO dataset is used to train and test the PolyDSS model. To address the issue of class imbalance, data augmentation techniques were used on the dataset. To investigate the impact of each technique on the model, extensive experiments were conducted. While the classification module achieved the highest accuracy of 0.9425 by utilizing the strength of ensemble learning using majority voting, the proposed segmenting module achieved the highest Dice Similarity Coefficient (DSC) of 0.9244 using ResUNet++ and LSF. In conjunction with the Paris classification system, the PolyDSS model, with its significant results, can assist clinicians in identifying polyps early and choosing the best approach to treatment. [ABSTRACT FROM AUTHOR] |
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