The deadliest type of skin cancer is malignant melanoma. The diagnosis requires at the earliest to reduce the mortality rate. In this study, an efficient Skin Melanoma Classification (SMC) system is presented using dermoscopic images as a non-invasive procedure. The SMC system consists of four modules; segmentation, feature extraction, feature reduction and finally classification. In the first module, k-means clustering is applied to cluster the colour information of dermoscopic images. The second module extracts meaningful and useful descriptors based on the statistics of local property, parameters of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model of wavelet and spatial patterns by Dominant Rotated Local Binary Pattern (DRLBP). The third module reduces the features by the t-test, and the last module uses deep learning for the classification. The individual performance shows that GARCH parameters of 3rd DWT level sub-bands provide 92.50% accuracy than local properties (77.5%) and DRLBP (88%) based features for the 1st stage (normal/abnormal). For the 2nd stage (benign/malignant), it is 95.83% (GRACH), 90% (DRLBP) and 80.8% (Local Properties). The selected 2% of features from the combination gives 99.5% and 100% for 1st and 2nd stage of the SMC system. The greatest degree of success is achieved on PH2 database images using two stages of deep learning. It can be used as a pre-screening tool as it provides 100% accuracy for melanoma cases.
The decrease in mortality rate due to skin cancer may be attributed to several treatment and detection factors. Due to the vast amount of research in both categories, significant advances have seen over the past 30 years. One of the prognostic factors for cancer cure is detection at the earliest. Currently, the advancement in imaging techniques and computerized system provides better results. The imaging techniques used for skin cancer diagnosis is dermoscopy, where a magnified visualization of the affected skin region is acquired. It shows the morphological structures that cannot be found by naked eyes. The accuracy of skin melanoma diagnosis has been improved with the use of many algorithms, such as ABCD rule [
Among the various components in the computerized system, feature extraction and classification are the main components. Many spatial and spectral features are utilized in the former steps, and many machine learning methods are developed in the later stage for the classification. Apart from ABCD rule [
Due to the advancement in multiresolution analysis, frequency domain features are added to the feature vector to increase accuracy further. Some of them are Discrete Wavelet Transform (DWT) [
The evaluation of deep learning [
This Section discusses the design of the SMC system. It consists of four sequential steps which are illustrated in
The exact skin cancer region is segmented using
Feature extraction aims to preserve the class discriminating information so that best class separation is achieved for least computational complexity. A classifier then uses these to decide whether the region is normal or abnormal. The advantage of extracting descriptors is that they will be a more compact representation of the segmented region than the image pixels alone if carefully chosen. The features are usually chosen based on the domain under consideration and in this study fall into three categories. The first group is based on the statistics of local property, and the second group consists of the parameters of the GARCH model of wavelet. Thirdly, spatial patterns of skin lesions are also recorded.
There are four local properties extracted in this study, mean (
The second moment is called variance and its positive square root is called
The third standardized central moment is skewness (
Using
To boost the performance of the SMC system, GARCH model is applied in the wavelet domain. The GARCH (p, q) for
and
where
At first, the dermoscopic images are transformed into DWT domain which is a powerful tool used in many pattern recognition techniques [
It is noted that the GARCH model is efficient only when the distribution of data has a heavy tail [
DRLBP [
where
It is evident from the
A large set of features makes the classification system extremely computationally intensive. The complexity of the SMC system increases more when the combination of features used in the classifiers. Thus a feature reduction step is necessary to eliminate the poor performing features that affect the classifier’s performance. The significant features are identified using
Let us consider features from two classes;
where
The non-linear relationship between the features of different classes can be modeled by neural networks which consist of input layers (number of features), hidden layer (normally 1) and output layer (number of classes). The information in the bracket shows the number of layers in each layer. The relationship between the features can be effectively modeled if the number of the hidden layer is increased. This is called deep learning [
The error between the actual and desired output is computed at first. The weights are updated iteratively while computing the error signal in the training phase. The update is done using the mean-squared error function. In the output layer, the error is multiplied using the sigmoid activation function. This process is stopped when the error is minimized at a predefined level by using the backpropagation algorithm. It is a descent algorithm that propagates the error from the output layer to lower layers. The weights are adjusted for the dampening oscillations with the help of learning rate and momentum factor so that the error rate is reduced in a decent direction. As the SMC system outputs a binary decision, a linear function is used in the output layer.
The developed SMC system is analyzed using publically available dermoscopic image databases; PH2 [
Description | Parameters |
---|---|
Name of the database | PH2 |
#Normal images | 80 |
#Benign images | 80 |
#Malignant images | 40 |
#Resolution | |
#Type of images | RGB |
The accuracy (
There is no misclassification for a perfect system, which means that sensitivity and specificity will both be 100%. A high sensitivity measure can lead to a decrease the mortality rate. These measures are computed using
As the GARCH features are extracted from DWT with many resolution levels of decomposition, the local properties and DRLBP are first analyzed independently. All normal images are considered a group of negative samples and abnormal images as positive samples in this stage. Then,
It is evident from the
DWT-L | Performance measures | ||||||
---|---|---|---|---|---|---|---|
1 | 100 | 20 | 70 | 10 | 83.33 | 87.50 | 85.00 |
2 | 103 | 17 | 72 | 8 | 85.83 | 90.00 | 87.50 |
3 | 109 | 11 | 76 | 4 | 90.83 | 95.00 | 92.50 |
4 | 105 | 15 | 73 | 7 | 87.50 | 91.25 | 89.00 |
It is observed that over 90% of accuracy is obtained by GARCH parameters extracted from the sub-bands of 3rd level DWT. It is well known that more information can be obtained when increasing the resolution levels. However, the features obtained from higher resolution levels reduce the system’s accuracy due to the redundant data that can be seen at 4th level DWT features. Also, it is evident from
Applying the 1st stage SMC system is reasonable while using the features independently but insufficient in the medical field that requires more accuracy to decrease the mortality. The redundant features in each group, which affects the performance, are eliminated by a feature reduction approach to obtain more accuracy.
SF (%) | Performance measures | ||||||
---|---|---|---|---|---|---|---|
1 | 115 | 5 | 79 | 1 | 95.83 | 98.75 | 97.00 |
2 | 119 | 1 | 80 | 0 | 99.17 | 100 | 99.50 |
3 | 112 | 8 | 78 | 2 | 93.33 | 97.50 | 95.00 |
After feature reduction, the highest performance is 99.17% sensitivity and 100% specificity for 2% selected features. With more features, both performance measures are reduced and thus the system select only 2% features from the combination of features as the best set to classify abnormal images.
It is evident from the 2nd stage SMC system; DRLBP has a maximum specificity of 86.8% and sensitivity of 92.5%.
DWT-L | Performance measures | ||||||
---|---|---|---|---|---|---|---|
1 | 35 | 5 | 72 | 8 | 87.50 | 90.00 | 89.17 |
2 | 36 | 4 | 75 | 5 | 90.00 | 93.75 | 92.50 |
3 | 38 | 2 | 77 | 3 | 95.00 | 96.25 | 95.83 |
4 | 36 | 4 | 74 | 6 | 90.00 | 92.50 | 91.67 |
The best features which perform better than any other GARCH features are extracted from the 3rd level. The sensitivity of 3rd level GARCH features is increased
SF-% | Performance measures | ||||||
---|---|---|---|---|---|---|---|
1 | 37 | 3 | 79 | 1 | 92.50 | 98.75 | 96.67 |
2 | 40 | 0 | 80 | 0 | 100.00 | 100.00 | 100.00 |
3 | 36 | 4 | 77 | 3 | 90.00 | 96.25 | 94.17 |
After feature reduction, the best performing features for 100% sensitivity and specificity are 2% features from the feature reduction approach.
To visually analyze the SMC system, which uses three types of features and a combination of these features, ROC is used.
An efficient SMC system which combines segmentation, feature extraction, feature reduction and classification stages into one automated operation is developed and investigated for skin cancer diagnosis. The use of local properties, GARCH parameters from 3rd DWT level sub-bands and DRLBP to classify skin melanoma images is tested. Deep learning is tested using PH2 database images and gives almost near-ideal system performance in terms of accuracy, sensitivity and specificity. Also, it is found that GARCH modelling can indeed be used for skin cancer diagnosis, and there are indeed performance differences in these features. The sensitivity of 1st stage and 2nd stage of the SMC system are 99.17% and 100% respectively, with all normal images are perfectly classified. The greatest degree of success is achieved on PH2 database images using two stages of deep learning. It can be used as a pre-screening tool as it provides 100% accuracy for melanoma cases.