Skin Lesion Semantic Segmentation Using Convolutional Encoder Decoder Architecture

2018 ◽  
Author(s):  
Suresh Sudam Kolekar ◽  
Pandurang Ganpati Magdum
Keyword(s):  
Skin Lesion ◽  
Semantic Segmentation ◽  
Decoder Architecture ◽  
Convolutional Encoder

scholarly journals Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129668-129678
Author(s):  
Muhammad Almas Anjum ◽  
Javaria Amin ◽  
Muhammad Sharif ◽  
Habib Ullah Khan ◽  
Muhammad Sheraz Arshad Malik ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
Lesion Classification

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

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5630
Author(s):  
Chin Yii Eu ◽  
Tong Boon Tang ◽  
Cheng-Hung Lin ◽  
Lok Hua Lee ◽  
Cheng-Kai Lu
Keyword(s):  
Colorectal Cancer ◽  
Automatic Segmentation ◽  
Hospital Setting ◽  
Decoder Architecture ◽  
The Future ◽  
Sensitivity Specificity ◽  
Convolutional Encoder ◽  
Cancer Côlon ◽  
Aided Diagnosis ◽  
Better Than

Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colorectal cancer. Colon screening is time-consuming and highly operator dependent. In view of this, a computer-aided diagnosis (CAD) method needs to be developed for the automatic segmentation of polyps in colonoscopy images. This paper proposes a modified SegNet Visual Geometry Group-19 (VGG-19), a form of convolutional neural network, as a CAD method for polyp segmentation. The modifications include skip connections, 5 × 5 convolutional filters, and the concatenation of four dilated convolutions applied in parallel form. The CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB databases were used to evaluate the model, and it was found that our proposed polyp segmentation model achieved an accuracy, sensitivity, specificity, precision, mean intersection over union, and dice coefficient of 96.06%, 94.55%, 97.56%, 97.48%, 92.3%, and 95.99%, respectively. These results indicate that our model performs as well as or better than previous schemes in the literature. We believe that this study will offer benefits in terms of the future development of CAD tools for polyp segmentation for colorectal cancer diagnosis and management. In the future, we intend to embed our proposed network into a medical capsule robot for practical usage and try it in a hospital setting with clinicians.

scholarly journals Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

2021 ◽  
Vol 45 (1) ◽  
pp. 122-129
Author(s):  
Dang N.H. Thanh ◽  
Nguyen Hoang Hai ◽  
Le Minh Hieu ◽  
Prayag Tiwari ◽  
V.B. Surya Prasath
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Computing Systems ◽  
Segmentation Methods ◽  
Dermoscopic Image ◽  
Better Than

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.

scholarly journals DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

2021 ◽  
Vol 12 ◽  
Author(s):  
Talha Ilyas ◽  
Muhammad Umraiz ◽  
Abbas Khan ◽  
Hyongsuk Kim
Keyword(s):  
Feature Selection ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Dynamic Feature ◽  
Feature Maps ◽  
Adaptive Feature Selection ◽  
Convolutional Encoder ◽  
Time Segmentation ◽  
Flow Of Information ◽  
Segmentation Models

Autonomous harvesters can be used for the timely cultivation of high-value crops such as strawberries, where the robots have the capability to identify ripe and unripe crops. However, the real-time segmentation of strawberries in an unbridled farming environment is a challenging task due to fruit occlusion by multiple trusses, stems, and leaves. In this work, we propose a possible solution by constructing a dynamic feature selection mechanism for convolutional neural networks (CNN). The proposed building block namely a dense attention module (DAM) controls the flow of information between the convolutional encoder and decoder. DAM enables hierarchical adaptive feature fusion by exploiting both inter-channel and intra-channel relationships and can be easily integrated into any existing CNN to obtain category-specific feature maps. We validate our attention module through extensive ablation experiments. In addition, a dataset is collected from different strawberry farms and divided into four classes corresponding to different maturity levels of fruits and one is devoted to background. Quantitative analysis of the proposed method showed a 4.1% and 2.32% increase in mean intersection over union, over existing state-of-the-art semantic segmentation models and other attention modules respectively, while simultaneously retaining a processing speed of 53 frames per second.

scholarly journals Neural networks for topology optimization

2019 ◽  
Vol 34 (4) ◽  
pp. 215-223 ◽  
Author(s):  
Ivan Sosnovik ◽  
Ivan Oseledets
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
High Performance ◽  
Current Method ◽  
Optimization Methods ◽  
Proposed Model ◽  
Significant Acceleration ◽  
Decoder Architecture ◽  
Segmentation Task ◽  
Convolutional Encoder

Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.

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