Abstract
PURPOSE: Squamous cell carcinoma (SCC) accounts for 90% of head and neck cancer. The majority of cases can be diagnosed and even treated with endoscopic examination and surgery. Deep learning models have been adopted for various medical endoscopy exams. However, few reports have been on deep learning algorithms for segmenting head and neck SCC. APPROACH: Head and neck SCC pre-treatment endoscopic images during 2016-2020 were collected from the Kaohsiung Veterans General Hospital Department of Otolaryngology-Head and Neck Surgery. We present a new modification of the neural architecture search-U-Net-based model called SCC-Net for segmenting our enrolled endoscopic photos. The modification included a new technique called "Learnable Discrete Wavelet Pooling" to design a new formulation that combines the outputs of different layers using a channel attention module and assigns weights based on their importance in the information flow. We also incorporated the cross-stage-partial design from CSPnet. The performance was compared with other eight state-of-the-art image segmentation models. RESULTS: We collected a total of 556 pathologically confirmed SCC photos. The new SCC-Net algorithm achieves a high mean intersection over union (mIOU) of 87.2%, accuracy of 97.17%, and recall of 97.15%. When comparing the performance of our proposed model with eight different state-of-the-art image segmentation artificial neural network models, our model performed best in mIOU, Dice similarity coefficient, accuracy, and recall. CONCLUSIONS: Our proposed SCC-Net architecture was able to successfully segment lesions from white light endoscopic images with promising accuracy, with a single model performing well in all upper aerodigestive tracts.