An improved framework for polyp image segmentation based on <scp>SegNet</scp> architecture

Afify, Heba M.; Mohammed, Kamel K.; Hassanien, Aboul Ella

doi:10.1002/ima.22568

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…At the segmentation stage, performance will be measured based on accuracy, sensitivity, and specificity values using (8) to (10) (Afify et al, 2021). The stages in liver research consist of data collection, preprocessing, and segmentation which can be seen in Figure 3.…”

Section: Discussionmentioning

confidence: 99%

Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease

Umilizah,

Octavia,

Kesuma

et al. 2024

JITE

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Liver disease is a disease caused by various factors such as the spread of viruses. Liver damage causes the ability to break down red blood cells to be disrupted. Detection of liver disease can be done using the segmentation. Segmentation is useful for separating an area of the liver in an image from other areas. Segmentation carried out manually requires experts and a long time, so automatic segmentation is needed. CNN can be used to perform automatic segmentation. One of the CNN architectures is the U-Net architecture. Segmentation requires quality images to improve recognition of image patterns, so image improvement is needed in the form of contrast enhancement. Contrast improvement was carried out by taking Green Channel images. Contrast enhancement was carried out using the Contrast Stretching and CLAHE methods. The image improvement results show MSE and SSIM values 66.1844 and 0.7088. Evaluation of the image improvements obtained provides significant changes. The improved image is used at the segmentation stage. Segmentation is carried out using the U-Net architecture. The segmentation results obtained performance evaluation values in the form of accuracy 99.6%, sensitivity 98.9%, and specificity 99.7%. This shows that the proposed method can detect liver disease in liver images well

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Section: Discussionmentioning

confidence: 99%

Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease

Umilizah,

Octavia,

Kesuma

et al. 2024

JITE

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“…Vgg16 is a classical convolutional neural network structure, pre-trained on the ImageNet dataset, and is widely used in image classification and feature extraction tasks [11]. In the pathological image classification task, we used the Vgg16 model to extract image features, including lower-level color and texture information and higher-level semantic information, to enhance the classification ability of the classifier.…”

Section: Methodsmentioning

confidence: 99%

Multi-TransPathoNet: a transfer learning-based neural network for pathology image classification with multi-task learning

Deng,

Li,

Ren

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model's performance.

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“…This abnormal growth occurs gradually and, in most instances, it does not create symptoms till it achieves a significant magnitude [6]. One of the most common causes of gastroenterology is a polyp, which could develop into colorectal cancer [7]. Nonetheless, cancer is preventable and treated, if polyps are diagnosed early [6].…”

Section: Introductionmentioning

confidence: 99%

Dual U-Net with Resnet Encoder for Segmentation of Medical Images

Nisa¹,

Ismail²

2022

IJACSA

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Segmentation of medical images has been the most demanding and growing area currently for analysis of medical images. Segmentation of polyp images is a huge challenge because of the variability of color depth and morphology in polyps throughout colonoscopy imaging. For segmentation, in this work, we have used a dataset of images of the gastrointestinal polyp. The algorithms used in this paper for segmentation of gastrointestinal polyp images depend on profound deep convolutional neural network architectures: FCN, Dual U-net with Resnet Encoder, U-net, and Unet_Resnet. To improve the performance, data augmentation is performed on the dataset. The efficiency of the algorithms is measured by using metrics such as Dice Similarity Coefficient (DSC) and Intersection Over Union (IOU). The algorithm Dual U-net with Resnet Encoder obtains a higher DSC of 0.87 and IOU of 0.80 and beats the other algorithms U-net, FCN, and Unet_Resnet in segmentation of gastrointestinal polyp images.

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An improved framework for polyp image segmentation based on SegNet architecture

Cited by 17 publications

References 38 publications

Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease

Combination of Image Improvement on Segmentation Using a Convolutional Neural Network in Efforts to Detect Liver Disease

Multi-TransPathoNet: a transfer learning-based neural network for pathology image classification with multi-task learning

Dual U-Net with Resnet Encoder for Segmentation of Medical Images

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