Skin Lesion Segmentation Based on Modification of SegNet Neural Networks

Ninh, Quoc Cuong; Tran, Thi-Thao; Tran, Tien; Tran, Thi Anh Xuan; Pham, Van-Truong

doi:10.1109/nics48868.2019.9023862

Cited by 17 publications

(4 citation statements)

References 17 publications

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“…The performance of the proposed model is compared against the various other approaches concerning the parameters like Accuracy, Sensitivity, and Specificity of each of the approaches like Decision Tree and Random Forest approaches, Lesion Index Calculation Unit (LICU) approach, Fuzzy Support Vector Machine with probabilistic boosting the segmentation, Compact Deep Neural Network, SegNet model, U-Net model, respectively [ 81 , 82 , 83 , 84 , 85 ], considered for comparative analysis that determine the efficiency of the model. Figure 10 is the graph that is obtained from the values of Table 4 .…”

Section: Resultsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

491

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Section: Resultsmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

491

169

View full text Add to dashboard Cite

show abstract

“…This work adopts a modified Segnet approach for segmenting the preprocessed image Img pre . The procedure of modified Segnet is as follows: Modified Segnet: Segnet [32] is a specialized deep learning architecture crafted specifically for semantic segmentation tasks, utilizing an encoder-decoder structure comprising CNNs and pooling layers to classify image pixels. In SegNet's encoder component, multiple convolutional layers are followed by pooling layers that extract features with high levels from the input image while decreasing spatial dimensions through downsampling operations, gradually PLOS ONE building hierarchical representations.…”

Section: Segmentationmentioning

confidence: 99%

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Shafi,

Chinnappan

2024

PLoS ONE

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Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases.

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“…The feature maps are produced by implementing convolution with a filter bank in each encoder network. Additionally, a recent study in [62] has proposed a modified SegNet for skin lesion segmentation. The authors have reduced the total learned parameter of the architecture by lowering the downsampling and upsampling layers of the original SegNet.…”

Section: B Recent Work On Deep Learning Approach To Skin Lesion Segmentationmentioning

confidence: 99%

Skin Lesions Classification and Segmentation: A Review

Stofa¹,

Zulkifley²,

Zainuri³

2021

IJACSA

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Skin Lesion Segmentation Based on Modification of SegNet Neural Networks

Cited by 17 publications

References 17 publications

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Skin Lesions Classification and Segmentation: A Review

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