Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions
The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning−based methods. We propose a new decision system based on multiple classifiers like neural networks and feature−based methods. Each classifier (method) gives the f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/6/1753 |
| _version_ | 1828117307803566080 |
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| author | Hassan El-Khatib Dan Popescu Loretta Ichim |
| author_facet | Hassan El-Khatib Dan Popescu Loretta Ichim |
| author_sort | Hassan El-Khatib |
| collection | DOAJ |
| description | The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning−based methods. We propose a new decision system based on multiple classifiers like neural networks and feature−based methods. Each classifier (method) gives the final decision system a certain weight, depending on the calculated accuracy, helping the system make a better decision. First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process. Some biostatistic parameters, such as accuracy, specificity, sensitivity, and Dice coefficient are calculated. Then, we developed three other methods based on convolutional neural networks (CNNs). The CNNs were pre-trained using large ImageNet and Places365 databases. GoogleNet, ResNet-101, and NasNet-Large, were used in the enumeration order. CNN architectures were fine-tuned in order to distinguish the different types of skin lesions using transfer learning. The accuracies of the classifications were determined. The last proposed method uses the classical method of image object detection, more precisely, the one in which some features are extracted from the images, followed by the classification step. In this case, the classification was done by using a support vector machine. Just as in the first method, the sensitivity, specificity, Dice similarity coefficient and accuracy are determined. A comparison of the obtained results from all the methods is then done. As mentioned above, the novelty of this paper is the integration of these methods in a global fusion-based decision system that uses the results obtained by each individual method to establish the fusion weights. The results obtained by carrying out the experiments on two different free databases shows that the proposed system offers higher accuracy results. |
| first_indexed | 2024-04-11T13:12:11Z |
| format | Article |
| id | doaj.art-04f31195c64a412d9f9817ee3308374a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:12:11Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-04f31195c64a412d9f9817ee3308374a2022-12-22T04:22:29ZengMDPI AGSensors1424-82202020-03-01206175310.3390/s20061753s20061753Deep Learning–Based Methods for Automatic Diagnosis of Skin LesionsHassan El-Khatib0Dan Popescu1Loretta Ichim2Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, RomaniaThe main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning−based methods. We propose a new decision system based on multiple classifiers like neural networks and feature−based methods. Each classifier (method) gives the final decision system a certain weight, depending on the calculated accuracy, helping the system make a better decision. First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process. Some biostatistic parameters, such as accuracy, specificity, sensitivity, and Dice coefficient are calculated. Then, we developed three other methods based on convolutional neural networks (CNNs). The CNNs were pre-trained using large ImageNet and Places365 databases. GoogleNet, ResNet-101, and NasNet-Large, were used in the enumeration order. CNN architectures were fine-tuned in order to distinguish the different types of skin lesions using transfer learning. The accuracies of the classifications were determined. The last proposed method uses the classical method of image object detection, more precisely, the one in which some features are extracted from the images, followed by the classification step. In this case, the classification was done by using a support vector machine. Just as in the first method, the sensitivity, specificity, Dice similarity coefficient and accuracy are determined. A comparison of the obtained results from all the methods is then done. As mentioned above, the novelty of this paper is the integration of these methods in a global fusion-based decision system that uses the results obtained by each individual method to establish the fusion weights. The results obtained by carrying out the experiments on two different free databases shows that the proposed system offers higher accuracy results.https://www.mdpi.com/1424-8220/20/6/1753image processingdeep learningmachine learningconvolutional neural networkartificial intelligenceskin lesion detectionneural networkdermoscopic image |
| spellingShingle | Hassan El-Khatib Dan Popescu Loretta Ichim Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions Sensors image processing deep learning machine learning convolutional neural network artificial intelligence skin lesion detection neural network dermoscopic image |
| title | Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions |
| title_full | Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions |
| title_fullStr | Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions |
| title_full_unstemmed | Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions |
| title_short | Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions |
| title_sort | deep learning based methods for automatic diagnosis of skin lesions |
| topic | image processing deep learning machine learning convolutional neural network artificial intelligence skin lesion detection neural network dermoscopic image |
| url | https://www.mdpi.com/1424-8220/20/6/1753 |
| work_keys_str_mv | AT hassanelkhatib deeplearningbasedmethodsforautomaticdiagnosisofskinlesions AT danpopescu deeplearningbasedmethodsforautomaticdiagnosisofskinlesions AT lorettaichim deeplearningbasedmethodsforautomaticdiagnosisofskinlesions |