-Score-Based Secure Biomedical Model for Effective Skin Lesion Segmentation Over eHealth Cloud

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-19
Author(s):  
Amitesh Singh Rajput ◽  
Vishesh Kumar Tanwar ◽  
Balasubramanian Raman
Keyword(s):  
Skin Lesion ◽  
Number System ◽  
Correction Method ◽  
Lesion Segmentation ◽  
Local Color ◽  
Partially Order ◽  
Patient’S Rights ◽  
Image Pixels ◽  
Encrypted Images ◽  
Cloud Servers

This study aims to process the private medical data over eHealth cloud platform. The current pandemic situation, caused by Covid19 has made us to realize the importance of automatic remotely operated independent services, such as cloud. However, the cloud servers are developed and maintained by third parties, and may access user’s data for certain benefits. Considering these problems, we propose a specialized method such that the patient’s rights and changes in medical treatment can be preserved. The problem arising due to Melanoma skin cancer is carefully considered and a privacy-preserving cloud-based approach is proposed to achieve effective skin lesion segmentation. The work is accomplished by the development of a Z -score-based local color correction method to differentiate image pixels from ambiguity, resulting the segmentation quality to be highly improved. On the other hand, the privacy is assured by partially order homomorphic Permutation Ordered Binary (POB) number system and image permutation. Experiments are performed over publicly available images from the ISIC 2016 and 2017 challenges, as well as PH dataset, where the proposed approach is found to achieve significant results over the encrypted images (known as encrypted domain), as compared to the existing schemes in the plain domain (unencrypted images). We also compare the results with the winners of the ISBI 2016 and 2017 challenges, and show that the proposed approach achieves a very close result with them, even after processing test images in the encrypted domain. Security of the proposed approach is analyzed using a challenge-response game model.

scholarly journals Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 811
Author(s):  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Tallha Akram ◽  
Robertas Damaševičius ◽  
Rytis Maskeliūnas
Keyword(s):  
Skin Lesion ◽  
Skin Lesions ◽  
Classification Performance ◽  
Lesion Segmentation ◽  
Local Color ◽  
Heat Map ◽  
Kernel Extreme Learning Machine ◽  
Deep Learning Features ◽  
Learning Machine ◽  
Thresholding Function

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

scholarly journals FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5172
Author(s):  
Yuying Dong ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Skin Lesion ◽  
Real Life ◽  
Good Deal ◽  
Experimental Tests ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Effective Feedback ◽  
Public Datasets ◽  
Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

scholarly journals Lightweight encoder-decoder model for automatic skin lesion segmentation

2021 ◽  
pp. 100640
Author(s):  
Adi Wibowo ◽  
Satriawan Rasyid Purnama ◽  
Panji Wisnu Wirawan ◽  
Hanif Rasyidi
Keyword(s):  
Skin Lesion ◽  
Lesion Segmentation

scholarly journals Reversible Data Hiding in Encrypted Images Using Median Edge Detector and Two’s Complement

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 921
Author(s):  
Rui Wang ◽  
Guohua Wu ◽  
Qiuhua Wang ◽  
Lifeng Yuan ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Personal Information ◽  
Rapid Development ◽  
Prediction Method ◽  
Prediction Errors ◽  
Edge Detector ◽  
Secret Data ◽  
Encrypted Images ◽  
Cloud Servers

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

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