Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks
This work addresses colorectal cancer detection by improving polyp classification accuracy, though it is incremental as it builds on existing sensor and network techniques.
The study tackled the problem of early detection miss rate of colorectal cancer polyps by proposing a hyper-sensitive vision-based tactile sensor and a dilated residual network for classification, achieving superior performance compared to state-of-the-art models like AlexNet and DenseNet.
In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet architecture, and the transfer learning concept applied on a small dataset with the scale of hundreds of images. The proposed tactile sensor provides high-resolution 3D textural images of CRC polyps that will be used for their accurate classification via the proposed dilated residual network. To collect realistic surface patterns of CRC polyps for training the ML models and evaluating their performance, we first designed and additively manufactured 160 unique realistic polyp phantoms consisting of 4 different hardness. Next, the proposed architecture was compared with the state-of-the-art ML models (e.g., AlexNet and DenseNet) and proved to be superior in terms of performance and complexity.