Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer

Ali, Karar; Shaikh, Zaffar Ahmed; Khan, Abdullah Ayub; Laghari, Asif Ali

doi:10.1016/j.neuri.2021.100034

Cited by 132 publications

(65 citation statements)

References 64 publications

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“…For instance, sometimes, patients receive medical treatment by random occurrence and sometimes as part of the medical management process [10]. Further, this happens when there is no data exchange between the connected systems (nodes), and incomplete data occurs during data exchange from different disjoint nodes because the same patients end up with two different medical records within the same e-healthcare applications [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…To ensure the validity, authentication, and reliability of e-healthcare systems and information processing and the overall management of records [9], it is imperatively significant to maintain the transparency and privacy of the complete process of medical data, information, knowledge, and wisdom over the network. To ascertain this, the identification of shreds of sensitive medical records is critical for the record-keeping purpose of an individual transaction that occurred while analyzing the medical data [10,11]. The complexity of hiding innumerable kinds of medical information in a carrier channel (through signals) over wireless network-based connected edge devices to exchange information.…”

Section: Introductionmentioning

confidence: 99%

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BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

Shaikh

Khan

Teng

et al. 2022

Wireless Communications and Mobile Computing

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This paper presents a layered hierarchy that depicts the progressive relationship between data, information, knowledge, and wisdom. To begin with, data is gathered and organized into information. Information is gathered, filtered, refined, and put through an investigation process to create knowledge. Wisdom is attained after knowledge discovery through the process of filtration and aggregation through experience. The layered hierarchy in the domain of e-healthcare necessitates higher scheduling costs for data collection, processing wisdom, and management, which is also an insecure and untrustworthy process for progressive medical service. The medical industry faces a difficult problem in providing collected data integrity, information reliability, and knowledge trustworthiness for the service of progressive medical relationships in the face of an increasing number of day-to-day records. The blockchain consortium hyperledger (fabric) has been used in this paper to act as a bridge that bridges the gap between electronic data, information, knowledge, and wisdom (DIKW) movement and processes by enabling the process of the layered hierarchy of schedule information and management and providing security and transparency. For e-healthcare information management and privacy, the DIKW-ledger, such as patients’ consultancy information, availing medical services, personal records, appointments, treatment details, and other health-related transactions, a consortium hyperledger fabric-enabled efficient architecture is proposed. This proposed architecture creates two networks: a public network for medical stakeholders to exchange and agree on specific medical activities before being preserved on distributed storage (read-only after record registration) and a private network for complete DIKW process scheduling and management. We designed and created smart contracts for this purpose, as well as use-case diagrams to describe the overall execution process. The proposed architectural solution provides more efficient information integrity, provenance, and storage procedures to immutably preserve the medical ledger in a permissioned hash-encrypted structure.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

Shaikh

Khan

Teng

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…According to them, to further improve the accuracy of forecasting, it makes sense to use different forecasting methods for different periods of the year. Other authors intend to create forecasts of solar radiation based on humidity and cloud cover, which are components of the clarity index [4][5][6][7][8][9]. Scientists in the following work managed to make an hourly forecast of solar radiation, thanks to machine learning technologies such as GRU and LSTM.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Stock Prices of Companies Producing Solar Panels Using Machine Learning Methods

et al. 2022

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Solar energy has become an integral part of the economy of developed countries, so it is important to monitor the pace of its development, prospects, as well as the largest companies that produce solar panels since the supply of solar energy in a particular country directly depends on them. The study analyzes the shares of Canadian Solar Inc. and First Solar Inc. The purpose of the study is to study the possibility of forecasting the stock price of solar energy companies using neural networks for the purpose of subsequent investment. The recurrent neural network LSTM is used in the article and this approach is based on complexity theory. Machine learning technologies are now being actively implemented in various sectors of the economy and are considered effective. The program used assigns different significance to the data of the last months and the data for the first months of the 1st year. The first year of the last 5 years of the company’s activity is taken as the first year since more distant data no longer have significant significance for the forecast. In the course of the study, a forecast of the stock price of Canadian Solar Inc. and First Solar Inc. for 245 days was obtained. Based on the results obtained, the following conclusions were made: 20 neurons of the network is not enough to make an accurate forecast, but the level of confidence in such a forecast is high enough, neural network forecasts are applicable in investing and are accurate enough to determine medium- and long-term trends, but these forecasts are not applicable for traders. The direction of improving the accuracy of neural network predictions is promising for further research.

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“…For example, the convolutional neural network’s (CNN’s) pre-trained architectures can effectively identify and remove the artifacts from the images such as noise. In medial image processing, especially in skin lesion recognition, it is essential to pre-process the image concerning feature selection and feature extraction leading to feature engineering to design an effective and correctly working algorithm [ 12 , 13 , 14 , 15 ]. The evolution of transfer learning and its advantages of saving resources with improved efficiency concerning cost and time-consuming issues have widely used CNN’s pre-trained networks in the image analysis research domain [ 2 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

Mohanty

Pradhan

Reddy³

et al. 2022

Cancers

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This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS.

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Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer

Cited by 132 publications

References 64 publications

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

Forecasting Stock Prices of Companies Producing Solar Panels Using Machine Learning Methods

Integrated Design of Optimized Weighted Deep Feature Fusion Strategies for Skin Lesion Image Classification

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