SAU-NET: Scale Aware Polyp Segmentation using Encoder-Decoder Network

Gautam, Anmol; Das, Suchana; Sharma, Pallabi; Maji, Pallab; Balabantaray, Bunil Kumar

doi:10.1109/tensymp54529.2022.9864338

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…The convolutional neural network is one of the most classic networks for processing visual mode classification tasks with simple structure and high training efficiency. Gautam et al successfully implemented the efficient recognition of classroom teaching videos using 2DCNN-LSTM and 3DCNN [20]; Due to its simple structure and excellent performance in similar tasks, our experiment chose 2DCNN-LSTM and 3DCNN models to test on the CH-CC dataset.…”

Section: Visual Modelsmentioning

confidence: 99%

CH-CC: A Chinese Multimodal Classroom Atmosphere Analysis Dataset Based on Teachers’ Behavior and Voice

Liao¹

2023

Preprint

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In this paper, we present a multimodal dataset for the analysis of classroom atmosphere, based on the behavior and voice of teachers in teaching scenarios. we propose four visual models, three audio models, and one visual-audio dual-modality model to be tested on our dataset. The results indicate that the CH-CC dataset is feasible and reliable and that the visual modality plays a major role in the analysis of this dataset.

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Section: Visual Modelsmentioning

confidence: 99%

CH-CC: A Chinese Multimodal Classroom Atmosphere Analysis Dataset Based on Teachers’ Behavior and Voice

Liao¹

2023

Preprint

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“…11,12 Moreover, even though the pooling operation provides some in-variance to the CNN model, in terms of the location of the desired feature, the popular pooling methods such as max and average pooling lose important spatial information as it cuts the spatial relationship. 13,14 For natural images, small details can be ignored, but for medical images such as colonoscopy still images, even the high or low values pixels, may contain significant information, leading to misclassification. To overcome this limitation, the proposed model elicits efficient features using a DWT pooling layer that also works efficiently in the presence of noise.…”

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confidence: 99%

LPNet: A lightweight CNN with discrete wavelet pooling strategies for colon polyps classification

Sharma

Das

Gautam

et al. 2022

Int J Imaging Syst Tech

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The traditional process of disease diagnosis from medical images follows a manual process, which is tedious and arduous. A computer‐aided diagnosis (CADs) system can work as an assistive tool to improve the diagnosis process. In this pursuit, this article introduces a unique architecture LPNet for classifying colon polyps from the colonoscopy video frames. Colon polyps are abnormal growth of cells in the colon wall. Over time, untreated colon polyps may cause colorectal cancer. Different convolutional neural networks (CNNs) based systems have been developed in recent years. However, CNN uses pooling to reduce the number of parameters and expand the receptive field. On the other hand, pooling results in data loss and is deleterious to subsequent processes. Pooling strategies based on discrete wavelet operations have been proposed in our architecture as a solution to this problem, with the promise of achieving a better trade‐off between receptive field size and computing efficiency. The overall performance of this model is superior to the others, according to experimental results on a colonoscopy dataset. LPNet with bio‐orthogonal wavelet achieved the highest performance with an accuracy of 93.55%. It outperforms the other state‐of‐the‐art (SOTA) CNN models for the polyps classification task, and it is lightweight in terms of the number of learnable parameters compared with them, making the model easily deployable in edge devices.

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PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning

Saad,

Maghraby,

Badawy

2023

Int J Comput Intell Syst

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A deep convolution neural network image segmentation model based on a cost-effective active learning mechanism is proposed and named PolySeg Plus. It is intended to address polyp segmentation with a lack of labeled data and a high false-positive rate of polyp discovery. In addition to applying active learning, which assisted in labeling more image samples, a comprehensive polyp dataset formed of five benchmark datasets was generated to increase the number of images. To enhance the captured image features, the locally shared feature method is used, which utilizes the power of employing neighboring features together with one another to improve the quality of image features and overcome the drawbacks of the Conditional Random Features method. Medical image segmentation was performed using ResUNet++, ResUNet, UNet++, and UNet models. Gaussian noise was removed from the images using a gaussian filter, and the images were then augmented before being fed into the models. In addition to optimizing model performance through hyperparameter tuning, grid search is used to select the optimum parameters to maximize model performance. The results demonstrated a significant improvement and applicability of the proposed method in polyp segmentation when compared to state-of-the-art methods on the datasets CVC-ClinicDB, CVC-ColonDB, ETIS Larib Polyp DB, KVASIR-SEG, and Kvasir-Sessile, with Dice coefficients of 0.9558, 0.8947, 0.7547, 0.9476, and 0.6023, respectively. Not only did the suggested method improve the dice coefficients on the individual datasets, but it also produced better results on the comprehensive dataset, which will contribute to the development of computer-aided diagnosis systems.

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SAU-NET: Scale Aware Polyp Segmentation using Encoder-Decoder Network

Cited by 7 publications

References 18 publications

CH-CC: A Chinese Multimodal Classroom Atmosphere Analysis Dataset Based on Teachers’ Behavior and Voice

CH-CC: A Chinese Multimodal Classroom Atmosphere Analysis Dataset Based on Teachers’ Behavior and Voice

LPNet: A lightweight CNN with discrete wavelet pooling strategies for colon polyps classification

PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning

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