Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection

Mohakud, Rasmiranjan; Dash, Rajashree

doi:10.1016/j.jksuci.2021.05.012

Cited by 56 publications

(40 citation statements)

References 21 publications

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“…Simulation of MBDFS-CPRRDLC technique and existing Automated Hyper-parameter Optimized CNN [1], Dermo-DOCTOR [2], and DCNN [3] are discussed. Based on the objective of the proposed MBDFS-CPRRDLC technique, experimental parameters such as Accuracy, precision, recall, F-measure and cancer detection time are selected for analyzing the performance of the proposed and existing method.…”

Section: Performance Results and Discussionmentioning

confidence: 99%

“…2 portrays the accuracy by the number of samples taken in the ranges from 1000 to 10000. As shown in the table, the accuracy of four methods namely MBDFS-CPRRDLC and existing Automated Hyper-parameter Optimized CNN [1], Dermo-DOCTOR [2], DCNN [3] are reported. The results noted that the MBDFS-CPRRDLC provides superior results when compared to conventional methods.…”

Section: Performance Results and Discussionmentioning

confidence: 99%

“…Experimental evaluation of the proposed MBDFS-CPRRDLC technique and existing Automated Hyper-parameter Optimized CNN [1], Dermo-DOCTOR [2], DCNN [3] are implemented in Java using Skin Cancer MNIST: HAM10000 taken from the Kaggle (https://www.kaggle.com/ kmader/skin-cancer-mnist-ham10000). Based on the objective of the proposed MBDFS-CPRRDLC technique i.e., focused on improving the skin cancer detection accuracy and detecting cancer at an earlier stage with minimum time the existing methods such as Automated Hyper-parameter Optimized CNN [1], Dermo-DOCTOR [2], and DCNN model [3] are taken as base paper. These two base papers are explained to understand the proposed method.…”

Section: Experimental Scenariomentioning

confidence: 99%

“…An Automated Hyper-Parameter Optimized Convolution Neural Net (CNN) was introduced in [1] for detecting the class of skin cancer. The Grey Wolf Optimization algorithm was applied by choosing the applicable hyper-parameters and improving the accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Performance Results and Discussionmentioning

confidence: 99%

Section: Performance Results and Discussionmentioning

confidence: 99%

Section: Experimental Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Sivakumar¹,

Ghorashi²,

Karim³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Evolutionary computation techniques have recently gained a lot of consideration as a vital metaheuristic family member. Moreover, several feature extraction and selection algorithms were used in medical diagnosis applications [25] , [26] , [27] . As a result, the goal of this paper is to build an efficient feature selection approach called EGWO-GA that is combined with deep CNN for improved diagnosis of CXR images.…”

Section: Introductionmentioning

confidence: 99%

CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer

Gopatoti

Vijayalakshmi²

2022

Biomedical Signal Processing and Control

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An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

Renith

Senthilselvi

2023

Int J Imaging Syst Tech

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Skin cancer is the most common deadly disease caused due to abnormal and uncontrolled growth of cells in the human body. According to a report, annually nearly one million people are affected by skin cancer worldwide. To protect human lives from such life‐threatening diseases, early identification of skin cancer is the only precautionary measure. In recent times, there already exist numerous automated techniques to detect and classify skin lesion malignancies using dermoscopic images. However, analyzing the dermoscopic images becomes an arduous task due to the presence of troublesome features such as light reflections, illumination variations, and uneven shape and dimension. To address the challenges faced during skin cancer recognition process, in this paper, we proposed an efficient intelligent automated system to detect and discriminate the dermoscopic images into malignant or benign. The proposed skin cancer detection model utilizes the HAM10000 dataset for evaluation. The dermoscopic images acquired from the HAM10000 dataset are initially preprocessed to enhance the quality of image and thus making it fit to train the classifier. Afterward, the most significant image patterns are extracted by the AlexNet architecture without any loss of detailed information. Later on, the extracted features are inputted to the proposed Improved Adaboost‐based Aphid–Ant Mutualism (IAB‐AAM) classification model to discriminate the images into malignant and benign categories. The proposed IAB‐AAM approach witnessed extensive enhancement in classification accuracy. The enhanced performance is attributed by integrating the AAM optimization concept with the IAB model. By comparing the performance of the proposed IAB‐AAM with other modern methods in terms of different evaluation indicators namely accuracy, precision, specificity, sensitivity, and f‐measure, the efficiency of the proposed IAB‐AAM technique is analyzed. From the experimental results, it is known that the proposed IAB‐AAM technique attains a greater accuracy rate of 95.7% in detecting skin cancer classes than other compared approaches.

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Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection

Cited by 56 publications

References 21 publications

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer

An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

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