RETRACTED ARTICLE: Deep learning based an automated skin lesion segmentation and intelligent classification model

Sikkandar, Mohamed Yacin; Alrasheadi, Bader; Prakash, N. B.; Hemalakshmi, G. R.; Mohanarathinam, A.; Shankar, K.

doi:10.1007/s12652-020-02537-3

Cited by 101 publications

(48 citation statements)

References 26 publications

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“…Inversely, VAE promotes the distribution of latent codes

to the standard normal distribution. Finally, latent codes

are comprised of stable statistical features which result in better convenience for decoder and efficacy of this model [41] . Next, decoder manages to reform the actual data by applying latent codes

Consequently, NN having the weight 0 indicates a conditional probability

.…”

Section: Unsupervised Learning Based Covid-19 Diagnosis Modelmentioning

confidence: 99%

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification

Mansour

Escorcia‐Gutierrez

Gamarra

et al. 2021

Pattern Recognition Letters

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At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively.

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“…Inversely, VAE promotes the distribution of latent codes

to the standard normal distribution. Finally, latent codes

are comprised of stable statistical features which result in better convenience for decoder and efficacy of this model [41] . Next, decoder manages to reform the actual data by applying latent codes

Consequently, NN having the weight 0 indicates a conditional probability

.…”

Section: Unsupervised Learning Based Covid-19 Diagnosis Modelmentioning

confidence: 99%

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification

Mansour

Escorcia‐Gutierrez

Gamarra

et al. 2021

Pattern Recognition Letters

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“…It was realized as a twostage cascaded framework with classifier and segmentation subnetwork models. Yacin et al [30] tested the efficacy of classification model for skin lesion diagnosis by combining a GrabCut algorithm and Adaptive Neuro-Fuzzy classifier (ANFC) model.…”

Section: Stephen Et Almentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Based Predictive Models for Health Informatics

Aloyuni¹

2021

JPRI

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Health informatics (HI) has become a significant research area due to the massive generation of digital health and medical data by biomedical and health research organizations. The health data sources are available in different forms namely electronic health records (EHRs), biomedical imaging, bio-signals, sensor data, genomic data, medical history, social media data, and so on. The structured health data can be utilized for HI and effective predictive modeling of health data assists in the decision-making process. The recently developed artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques pave a way for effective predictive modeling on health data. Numerous existing works have been presented in the literature depending upon the ML and DL based HI for various applications. With this motivation, this study aims to review the recent state of art ML and DL based predictive models for health sector. This survey primarily identifies the difference between the ML and DL architectures with their significance in health sector. In addition, the existing works are extensively reviewed and compared in terms of different aspects such as objectives, underlying methodology, input source, dataset used, performance validation, metrics, and so on. Finally, the open challenges and future scope of the HI are examined in detail. At the end of the survey, the readers find it useful to identify the present research and possible future scope of the ML and DL based predictive models for HI.

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“…The segmentation was performed using a fully resolved CNN (FrCNN), and four pre-trained networks were considered for the final classification. Sikkandar et al [25] presented a computerized method for the segmentation and classification of skin lesions using traditional techniques. The authors combined the performance of the GrabCut and Neuro Fuzzy (NF) classifier for the final classification.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Bibi¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy

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RETRACTED ARTICLE: Deep learning based an automated skin lesion segmentation and intelligent classification model

Cited by 101 publications

References 26 publications

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification

Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification

A Systematic Review on Machine Learning and Deep Learning Based Predictive Models for Health Informatics

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

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