Skin Cancer Detection Using Kernel Fuzzy C‐Means and Improved Neural Network Optimization Algorithm

Jia, Haitao; Zhao, Junwei; Molk, Ali Mohammad Norouzzadeh Gil

doi:10.1155/2021/9651957

Cited by 9 publications

(2 citation statements)

References 31 publications

(30 reference statements)

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“…Two common information resources for constructing the fuzzy models are the previous data and knowledge. A fuzzy system is comprised of four sections [32][33][34]: (1) fuzzy system, (2) fuzzy rules, (3) inference motor, and (4) defuzzification system. The abstract scheme of the proposed FS for calculating the existence of CH is shown in Figure 2.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

et al. 2022

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In today’s world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node; after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-II systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

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Section: The Proposed Methodsmentioning

confidence: 99%

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

et al. 2022

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“…This issue was addressed by implementing data sampling techniques. Both size and class imbalances are effectively mitigated by these techniques, ensuring that all image categories carry equal weight, resulting in the following data shape: (46,935,28,28,3). The shape of the data indicates that after the balancing process, there were 46,935 final images in the dataset.…”

Section: Dataset Preprocessingmentioning

confidence: 99%

Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction

Ismail,

Alsalamah

2023

Mathematics

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The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable breakthrough in image analysis using convolutional neural networks (CNNs) has emerged, specifically in the identification of skin cancer from images. Unfortunately, manually designing such neural architectures is prone to errors and consumes substantial time. It has become increasingly popular to design and fine-tune neural networks by using metaheuristic algorithms that are based on natural phenomena. A nature-inspired algorithm is a powerful alternative to traditional algorithms for solving problems, particularly in complex optimization tasks. One such algorithm, the Harris hawk optimization (HHO), has demonstrated promise in automatically identifying the most appropriate solution across a wide range of possibilities, making it suitable for solving complex optimization problems. The purpose of this study is to introduce a novel automated architecture called “HHOForSkin” that combines the power of convolutional neural networks with meta-heuristic optimization techniques. The HHOForSkin framework uses an innovative custom CNN architecture with 26 layers for the analysis of medical images. In addition, a Harris hawk optimization algorithm (HHO) is used to fine-tune the developed model for multiple skin cancer classification problems. The developed model achieves an average accuracy of 99.1% and 98.93% F1 score using a publicly available skin cancer dataset. These results position the developed optimization-based skin cancer detection strategy at the forefront, offering the highest accuracy for seven-class classification problems compared to related works.

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Deep Learning for Skin Lesion Segmentation: A Review and Outlook

Yang,

Gao,

Chen

et al. 2024

Lecture Notes in Electrical Engineering

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Skin Cancer Detection Using Kernel Fuzzy C‐Means and Improved Neural Network Optimization Algorithm

Cited by 9 publications

References 31 publications

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction

Deep Learning for Skin Lesion Segmentation: A Review and Outlook

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