Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

Shixiang, Zhang; Huang, Shuaiqi; Wu, Hongkai; Yang, Zicong; Chen, Yinda

doi:10.1155/2021/8700506

Cited by 6 publications

(5 citation statements)

References 65 publications

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“…Also, the authors obtained 96.74% of Se, 97.21% of Sp, and 97.36% of Acc on HAM 10000 dataset skin images. Shixiang et al (2021) used deep learning‐based Internet of things (IoT) for the detection of abnormal melanoma skin images. The classified melanoma process experimental results were tele‐transmitted to the remote unit using IoT process.…”

Section: Literature Surveymentioning

confidence: 99%

“…Also, the authors obtained 96.74% of Se, 97.21% of Sp, and 97.36% of Acc on HAM 10000 dataset skin images. Shixiang et al (2021) This weight index factor was determined by the computational cost of the entire execution of the proposed method in this work. Zhang et al (2019) detected intra-class variation of the skin images using residual learning convolutional neural network (ARL-CNN) classification algorithm.…”

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confidence: 99%

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Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method

Bharathi¹,

Malleswaran

Muthupriya

2023

Microscopy Res & Technique

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Detection and diagnosis of melanoma skin cancer is important to save the life of humans. The main objective of this article is to perform both detection and diagnosis of the skin cancers in dermoscopy images. Both skin cancer detection and diagnosis system uses deep learning architectures for the effective performance improvement as the main objective. The detection process involves by identifying the cancer affected skin dermoscopy images and the diagnosis process involves by estimating the severity levels of the segmented cancer regions in skin images. This article proposes parallel CNN architecture for the classification of skin images into either melanoma or healthy. Initially, color map histogram equalization (CMHE) method is proposed in this article to enhance the source skin images and then thick and thin edges are detected from the enhanced skin image using the Fuzzy system. The gray‐level co‐occurrence matrix (GLCM) and Law's texture features are extracted from the edge detected images and these features are optimized using genetic algorithm (GA) approach. Further, the optimized features are classified by the developed pipelined internal module architecture (PIMA) of deep learning structure. The cancer regions in the classified melanoma skin images are segmented using mathematical morphological process and these segmented cancer regions are diagnosed into either mild or severe using the proposed PIMA structure. The proposed PIMA‐based skin cancer classification system is applied and tested on ISIC and HAM 10000 skin image datasets. Research Highlights The melanoma skin cancer is detected and classified using dermoscopy images. The skin dermoscopy images are enhanced using color map histogram equalization. GLCM and Law's texture features are extracted from the enhanced skin images. To propose pipelined internal module architecture (PIMA) for the classification of skin images.

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Section: Literature Surveymentioning

confidence: 99%

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confidence: 99%

Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method

Bharathi¹,

Malleswaran

Muthupriya

2023

Microscopy Res & Technique

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“…In [13], the lesion segmenting approach was modeled as a Markov decision process, solving it by training an agent for segmenting regions utilizing a deep RL method, learned in continual action space, and utilizing the deep deterministic policy gradient technique. In [14], a new hybrid ML method for the detection of melanoma was proposed. This method used traditional ML approaches, including XGBoost supervised ML, CNNs, and EfficientNet.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model

Ananth

Mohananthini³

2023

Eng. Technol. Appl. Sci. Res.

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Blockchain (BC) and Machine learning (ML) technologies have been investigated for potential applications in medicine with reasonable success to date. On the other hand, as accurate and early diagnosis of skin lesion classification is essential to gradually increase the survival rate of the patient, Deep-Learning (DL) and ML technologies were introduced for supporting dermatologists to overcome these challenges. This study designed a Blockchain Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using an Optimal Deep Learning (BHESKD-ODL) model. The presented BHESKD-ODL model achieves security and proper classification of skin lesion images using BC to store the medical images of the patients to restrict access to third-party users or intruders. In addition, the BHESKD-ODL method secures the medical images using the mayfly optimization (MFO) algorithm with the Homomorphic Encryption (HE) technique. For skin lesion diagnosis, the proposed BHESKD-ODL method uses pre-processing and the Adam optimizer with a Fully Convolutional Network (FCN) based segmentation process. Furthermore, a radiomics feature extraction with a Bidirectional Recurrent Neural Network (BiRNN) model was employed for skin lesion classification. Finally, the Red Deer Optimization (RDO) algorithm was used for the optimal hyperparameter selection of the BiRNN approach. The experimental results of the BHESKD-ODL system on a benchmark skin dataset proved its promising performance in terms of different measures.

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“…But the accuracy was not enhanced using self-learning scheme. A new hybrid machine learning approach was developed in [16] for various cases of melanoma skin cancer detection. But it failed to improve the model efficiency and performance of melanoma detection.…”

Section: Related Workmentioning

confidence: 99%

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Sivakumar¹,

Ghorashi²,

Karim³

et al. 2022

Computers, Materials &Amp; Continua

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Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient's essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these existing problems, a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification (MBDFS-CPRRDLC) technique is introduced for detecting cancer at an earlier stage. The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input, hidden, and output for feature selection and classification. The patient information is composed of IoT. The patient information was stored in mobile clouds server for performing predictive analytics. The collected data are sent to the recurrent deep learning classifier. In the first hidden layer, the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption. Followed by, the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data. This process is repeatedly performed until the error gets minimized. In this way, disease classification is accurately performed with higher accuracy. Experimental evaluation is carried out for factors namely Accuracy, precision, recall, F-measure, as well as cancer detection time, by the amount of patient data. The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.

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Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

Cited by 6 publications

References 65 publications

Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method

Detection and diagnosis of melanoma skin cancers in dermoscopic images using pipelined internal module architecture (PIMA) method

Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

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