Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique
Skin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis...
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AIMS Press
2023-01-01
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| Series: | AIMS Electronics and Electrical Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/electreng.2023005?viewType=HTML |
| _version_ | 1797824289297137664 |
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| author | J. Rajeshwari M. Sughasiny |
| author_facet | J. Rajeshwari M. Sughasiny |
| author_sort | J. Rajeshwari |
| collection | DOAJ |
| description | Skin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis of skin lesions is not insignificant, slightly worse than that of dermatologists, and costly and time-consuming. Various machine learning algorithms have been used to detect the severity of the disease diagnosis. But it is more complex when detecting the disease. To overcome these issues, a modified Probabilistic Neural Network (MPNN) classifier has been proposed to determine the severity of skin cancer. The proposed method contains two phases such as training and testing the data. The collected features from the data of infected people are used as input to the modified PNN classifier in the current model. The neural network is also trained using Spider Monkey Optimization (SMO) approach. For analyzing the severity level, the classifier predicts four classes. The degree of skin cancer is determined depending on classifications. According to findings, the system achieved a 0.10% False Positive Rate (FPR), 0.03% error and 0.98% accuracy, while previous methods like KNN, NB, RF and SVM have accuracies of 0.90%, 0.70%, 0.803% and 0.86% correspondingly, which is lesser than the proposed approach. |
| first_indexed | 2024-03-13T10:36:45Z |
| format | Article |
| id | doaj.art-237ee3af76e547e89c29619bb1b2b9d1 |
| institution | Directory Open Access Journal |
| issn | 2578-1588 |
| language | English |
| last_indexed | 2024-03-13T10:36:45Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Electronics and Electrical Engineering |
| spelling | doaj.art-237ee3af76e547e89c29619bb1b2b9d12023-05-18T05:44:00ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882023-01-0171759910.3934/electreng.2023005Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization techniqueJ. Rajeshwari 0M. Sughasiny1Department of Computer Science, Srimad Andavan Arts and Science College, (Affiliated to Bharathidasan University), Srirangam, Thiruvanaikoil, Tiruchirappalli, Tamil Nadu 620005, IndiaDepartment of Computer Science, Srimad Andavan Arts and Science College, (Affiliated to Bharathidasan University), Srirangam, Thiruvanaikoil, Tiruchirappalli, Tamil Nadu 620005, IndiaSkin cancer is a pandemic disease now worldwide, and it is responsible for numerous deaths. Early phase detection is pre-eminent for controlling the spread of tumours throughout the body. However, existing algorithms for skin cancer severity detections still have some drawbacks, such as the analysis of skin lesions is not insignificant, slightly worse than that of dermatologists, and costly and time-consuming. Various machine learning algorithms have been used to detect the severity of the disease diagnosis. But it is more complex when detecting the disease. To overcome these issues, a modified Probabilistic Neural Network (MPNN) classifier has been proposed to determine the severity of skin cancer. The proposed method contains two phases such as training and testing the data. The collected features from the data of infected people are used as input to the modified PNN classifier in the current model. The neural network is also trained using Spider Monkey Optimization (SMO) approach. For analyzing the severity level, the classifier predicts four classes. The degree of skin cancer is determined depending on classifications. According to findings, the system achieved a 0.10% False Positive Rate (FPR), 0.03% error and 0.98% accuracy, while previous methods like KNN, NB, RF and SVM have accuracies of 0.90%, 0.70%, 0.803% and 0.86% correspondingly, which is lesser than the proposed approach.https://www.aimspress.com/article/doi/10.3934/electreng.2023005?viewType=HTMLmodified pnn classifieroptimal weight selection through spider monkey optimization (smo) algorithmskin cancer severity detection |
| spellingShingle | J. Rajeshwari M. Sughasiny Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique AIMS Electronics and Electrical Engineering modified pnn classifier optimal weight selection through spider monkey optimization (smo) algorithm skin cancer severity detection |
| title | Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique |
| title_full | Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique |
| title_fullStr | Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique |
| title_full_unstemmed | Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique |
| title_short | Modified PNN classifier for diagnosing skin cancer severity condition using SMO optimization technique |
| title_sort | modified pnn classifier for diagnosing skin cancer severity condition using smo optimization technique |
| topic | modified pnn classifier optimal weight selection through spider monkey optimization (smo) algorithm skin cancer severity detection |
| url | https://www.aimspress.com/article/doi/10.3934/electreng.2023005?viewType=HTML |
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