Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture

Eu, Chin Yii; Tang, Tong Boon; Lin, Cheng-Hung; Lee, Lok Hua; Lu, Cheng-Kai

doi:10.3390/s21165630

Cited by 8 publications

(1 citation statement)

References 37 publications

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“…The integration of AI and healthcare has played a crucial role in fostering partnerships for the goals of sustainable development. The smart medical applications have made significant contributions, enabling the identification of pathology differences that doctors may find challenging to diagnose [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine

Yu,

Lin,

et al. 2023

Sensors

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Artificial intelligence has revolutionised smart medicine, resulting in enhanced medical care. This study presents an automated detector chip for age-related macular degeneration (AMD) using a support vector machine (SVM) and three-dimensional (3D) optical coherence tomography (OCT) volume. The aim is to assist ophthalmologists by reducing the time-consuming AMD medical examination. Using the property of 3D OCT volume, a modified feature vector connected method called slice-sum is proposed, reducing computational complexity while maintaining high detection accuracy. Compared to previous methods, this method significantly reduces computational complexity by at least a hundredfold. Image adjustment and noise removal steps are excluded for classification accuracy, and the feature extraction algorithm of local binary patterns is determined based on hardware consumption considerations. Through optimisation of the feature vector connection method after feature extraction, the computational complexity of SVM detection is significantly reduced, making it applicable to similar 3D datasets. Additionally, the design supports model replacement, allowing users to train and update classification models as needed. Using TSMC 40 nm CMOS technology, the proposed detector achieves a core area of 0.12 mm2 while demonstrating a classification throughput of 8.87 decisions/s at a maximum operating frequency of 454.54 MHz. The detector achieves a final testing classification accuracy of 92.31%.

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Section: Introductionmentioning

confidence: 99%

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine

Yu,

Lin,

et al. 2023

Sensors

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A Novel Knowledge Distillation Technique for Colonoscopy and Medical Image Segmentation

Kar,

Mukhopadhyay,

Balaiwar

et al. 2023

Smart Innovation, Systems and Technologies

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Ψnet: a parallel network with deeply coupled spatial and squeezed features for segmentation of medical images

Elmeslimany,

Kishk,

Altantawy

2023

Multimed Tools Appl

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The process of delineating a region of interest or an object in an image is called image segmentation. Efficient medical image segmentation can contribute to the early diagnosis of illnesses, and accordingly, patient survival possibilities can be enhanced. Recently, deep semantic segmentation methods demonstrate state-of-the-art (SOTA) performance. In this paper, we propose a generic novel deep medical segmentation framework, denoted as Ψnet. This model introduces a novel parallel encoder-decoder structure that draws up the power of triple U-Nets. In addition, a multi-stage squeezed-based encoder is employed to raise the network sensitivity to relevant features and suppress the unnecessary ones. Moreover, atrous spatial pyramid pooling (ASPP) is employed in the bottleneck of the network which helps in gathering more effective features during the training process, hence better performance can be achieved in segmentation tasks. We have evaluated the proposed Ψnet on a variety of challengeable segmentation tasks, including colonoscopy, microscopy, and dermoscopy images. The employed datasets include Data Science Bowl (DSB) 2018 challenge as a cell nuclei segmentation from microscopy images, International Skin Imaging Collaboration (ISIC) 2017 and 2018 as skin lesion segmentation from dermoscopy images, Kvasir-SEG, CVC-ClinicDB, ETIS-LaribDB, and CVC-ColonDB as polyp segmentation from colonoscopy images. Despite the variety in the employed datasets, the proposed model, with extensive experiments, demonstrates superior performance to advanced SOTA models, such as U-Net, ResUNet, Recurrent Residual U-Net, ResUNet++, UNet++, BCDU-Net, MultiResUNet, MCGU-Net, FRCU-Net, Attention Deeplabv3p, DDANet, ColonSegNet, and TMD-Unet.

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Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture

Cited by 8 publications

References 37 publications

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine

Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine

A Novel Knowledge Distillation Technique for Colonoscopy and Medical Image Segmentation

Ψnet: a parallel network with deeply coupled spatial and squeezed features for segmentation of medical images

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