Research Article: Classification of skin lesions using transfer learning and augmentation with Alex-net

Date Published: May 21, 2019

Publisher: Public Library of Science

Author(s): Khalid M. Hosny, Mohamed A. Kassem, Mohamed M. Foaud, Jie Zhang.

http://doi.org/10.1371/journal.pone.0217293

Abstract

Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic keratosis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS—DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.

Partial Text

Skin cancer is one of the most-deadly kinds of cancers [1]. Essentially, melanoma and non-melanoma are the most known skin cancer types [2]. Death rate and incidence have increased significantly in last years because of melanoma lesions. The rate of curing can reach over 90% where physicians would save patients’ life if these lesions were detected in early stage [3]. Commonly, visual examination of skin cancer is difficult and may lead to wrong detection of lesions because there is a high similarity between different types of skin lesions (melanoma and non-melanoma) [4]. Therefore, the automatic classification of skin lesion images by using the image processing techniques and artificial intelligence is a successful alternative solution of the visual examination [5].

DCNN consist of neural networks, which have a number of convolutional layers to extract features from images and classify these images [28]. The difference between the original data used to train DCNN and the data used for testing will be minimized in the training phase with different scale or size but with the same feature. The feature can be extracted and classified using deep network well [29]. So DCNN can be used in the task of skin lesion detection and classification. The reasons behind that is noise, aberrations, and artefacts in addition to limitation of labeled images. Another reason is that dermoscopic images may have large variation for same features plus the visual similarity of different type of lesions. So, a large dataset must be used with DCNN for training to overcome these challenge [30,31].

In this section, the proposed method to classify the colored images for skin lesions is described. This section is divided into two subsections. The augmentation process for the colored images is presented in the first subsection. The process of transfer learning which is applied to the deep network is described in the second subsection.

Experiments are performed using an IBM computer equipped with a core i5 processor, 8 GB DDRAM and a NVIDIA GeForce 920M graphic card. The MATLAB 2017 x64-bit is used to execute the coded program. Three datasets, ISIC, MED-NODE, and DermIS—DermQuest, of RGB colored skin images are used in these experiments. The first dataset consists of three labeled data/classes, melanoma, seborrheic keratosis, and nevus. The second and the third datasets consist of only two labeled data/classes, melanoma and nevus. The code is converted from MATLAB 2017 to CUDA to be run over GPU. Using GPU enables us to use a huge number of training data with low error rate of models. In many works, like that with DCNN, the classification layer may be dropped out and replaced with other classification methods like multi-class SVM. In this work, the classification layer called softmax is replaced with a new softmax layer to be appropriate for skin lesion where three classes are used. Fig 2 illustrates the modified pre-trained AlexNet with the new softmax layer.

The performance of the proposed method is compared with the performance of the existing skin cancer classification methods [14–18,22–27]. The three datasets, DermIS- DermQuest, MED-NODE, and ISIC are used in this comparison. The comparative study has been done using the results as they appear in the corresponding papers. The accuracy measure and ROC curves are used as a quantative and qualitative measures to compare the performance of the different methods. The comparative study is divided into three groups based on the used dataset. In the first group, the performance of the proposed method is compared using the DermIS- DermQuest dataset. In the second group, the performance of the proposed method is compared with the performance of the existing methods [17, 18, 22, 23 and 24] using the MED-NODE dataset. The dataset, ISIC, is used in the last group where the performance of the proposed method is compared with the performance of the existing methods [25,26 and 27]. The obtained results of the first group using the DermIS- DermQuest dataset are shown in Table 2. The obtained results are visualized and displayed in Fig 6. The ROC for the proposed method and the existing classification methods [14–16] are displayed in Fig 7.

To build a new deep neural network with high performance, a huge number of labeled images is needed. The proposed method applies the transfer learning in three different ways to pre-trained architecture. The classification layer of AlexNet is replaced by softmax layer to classify the skin lesion into two or three classes. Based on its flexible architecture, it can be used to classify skin lesions into more classes. The weights are fine-tuned and the datasets are augmented by different rotation angles to overcome the problem of overfitting. The performance of the proposed method is tested using three datasets, DermIS- DermQuest, MED-NODE, and ISIC using GPU. The average accuracy, average sensitivity, average specificity, and average precision for the proposed method with the DermIS- DermQuest are 96.86%, 96.90%, 96.90%, and 96.92% respectively. For the MED-NODE dataset the average values of the performance measures are 97.70%, 97.34%, 97.34%, and 97.93%, respectively. The average performance measures with the ISIC dataset are 95.91%, 88.47%, 93.00%, and 92.34%. The experimental results show that the proposed skin lesion classification method outperforms several state-of-the-art classification methods.

 

Source:

http://doi.org/10.1371/journal.pone.0217293

 

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