A deep convolutional neural network for the detection of polyps in colonoscopy images. (July 2021) - Rahim, Tariq; Hassan, Syed Ali; Shin, Soo Young
Highlights: Provides a deep convolutional neural network-based model for the computerized detection of polyps within colonoscopy images. The proposed model comprises 16 convolutional layers with 2 fully connected layers, and a SoftMax layer. We applied two different activation functions, MISH and rectified linear unit activation functions for deeper propagation of information and self-regularized smooth non-monotonicity. Furthermore, we used a generalized intersection of union, thus overcoming issues such as scale invariance, rotation, and shape. Detailed benchmarked results are provided, showing better performance in terms of precision, sensitivity, F1-score, F2-score, and Dice-coefficient, thus proving the efficacy of the proposed model. Abstract: Colonic polyps detection remains an unsolved issue because of the wide variation in the appearance, texture, color, size, and appearance of the multiple polyp-like imitators during the colonoscopy process. In this paper, a deep convolutional neural network (CNN) based model for the computerized detection of polyps within colonoscopy images is proposed. The proposed deep CNN model employs a unique way of adopting different convolutional kernels having different window sizes within the same hidden layer for deeper feature extraction. A lightweight model comprising 16 convolutional layers with 2 fully connected layers (FCN), and a Softmax layer as output layer is implemented. For achieving a deeper propagation of information, self-regularized smooth non-monotonicity, and to avoid saturation during training, MISH as an activation function is used in the first 15 layers followed by the rectified linear unit activation (ReLU) function. Moreover, a generalized intersection of the union (GIoU) approach is employed, overcoming issues such as scale invariance, rotation, and shape encountering with IoU. Data augmentation techniques such as photometric and geometric distortions are employed to overcome the scarcity of the data set of the colonic polyp. Detailed experimental results are provided that are bench-marked with the MICCAI 2015 challenge and other publicly available data set reflecting better performance in terms of precision, sensitivity, F1-score, F2-score, and Dice-coefficient, thus proving the efficacy of the proposed model.