Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique
Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This researc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/21/3313 |
| _version_ | 1797632097132740608 |
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| author | Chandran Kaushik Viknesh Palanisamy Nirmal Kumar Ramasamy Seetharaman Devasahayam Anitha |
| author_facet | Chandran Kaushik Viknesh Palanisamy Nirmal Kumar Ramasamy Seetharaman Devasahayam Anitha |
| author_sort | Chandran Kaushik Viknesh |
| collection | DOAJ |
| description | Human skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio. |
| first_indexed | 2024-03-11T11:32:03Z |
| format | Article |
| id | doaj.art-cf4b554acc9249de9684d3b1875e1be0 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T11:32:03Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-cf4b554acc9249de9684d3b1875e1be02023-11-10T15:00:56ZengMDPI AGDiagnostics2075-44182023-10-011321331310.3390/diagnostics13213313Detection and Classification of Melanoma Skin Cancer Using Image Processing TechniqueChandran Kaushik Viknesh0Palanisamy Nirmal Kumar1Ramasamy Seetharaman2Devasahayam Anitha3Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, IndiaDepartment of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, IndiaDepartment of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, IndiaDepartment of Science and Humanities, Karpagam Institute of Technology, Coimbatore 641105, IndiaHuman skin cancer is the most common and potentially life-threatening form of cancer. Melanoma skin cancer, in particular, exhibits a high mortality rate. Early detection is crucial for effective treatment. Traditionally, melanoma is detected through painful and time-consuming biopsies. This research introduces a computer-aided detection technique for early melanoma diagnosis-sis. In this study, we propose two methods for detecting skin cancer and focus specifically on melanoma cancerous cells using image data. The first method employs convolutional neural networks, including AlexNet, LeNet, and VGG-16 models, and we integrate the model with the highest accuracy into web and mobile applications. We also investigate the relationship between model depth and performance with varying dataset sizes. The second method uses support vector machines with a default RBF kernel, using feature parameters to categorize images as benign, malignant, or normal after image processing. The SVM classifier achieved an 86.6% classification accuracy, while the CNN maintained a 91% accuracy rate after 100 compute epochs. The CNN model is deployed as a web and mobile application with the assistance of Django and Android Studio.https://www.mdpi.com/2075-4418/13/21/3313convolutional neural networkDjangomelanomaskin cancersupport vector machine |
| spellingShingle | Chandran Kaushik Viknesh Palanisamy Nirmal Kumar Ramasamy Seetharaman Devasahayam Anitha Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique Diagnostics convolutional neural network Django melanoma skin cancer support vector machine |
| title | Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique |
| title_full | Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique |
| title_fullStr | Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique |
| title_full_unstemmed | Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique |
| title_short | Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique |
| title_sort | detection and classification of melanoma skin cancer using image processing technique |
| topic | convolutional neural network Django melanoma skin cancer support vector machine |
| url | https://www.mdpi.com/2075-4418/13/21/3313 |
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