1. Introduction

2. The Practice Standards for Medical Imaging

3. Literature Review

4. Methodology

4.4. DCGAN Architecture

Deep learning is gaining recognition in image classification because it can extract and choose more features than traditional techniques [95, 96]. However, large, labelled datasets are required to maximize deep learning's potential. Researchers have reported that dermoscopy images are often unlabeled or under-labeled, necessitating expert annotations. GANs are a new and intriguing method that has recently appeared to solve unlabeled datasets [97-99]. The ability of GANs to generate precise synthetic data has recently increased their prominence. Also, GANs are seen to be a promising approach to the problem of accurately classifying data with low levels of inter-class variation. For learning or sampling complicated probability distributions of image data, GANs are a popular generative model type.

Many researchers have implemented DCGAN on various real-world applications and have produced significantly good results in data synthesis and classification [100]. GANs are prone to instability and might be difficult to train. Finding the ideal balance might be challenging because the training involves a minmax game between the generator and discriminator networks. It is common for GANs to suffer from mode collapse, which occurs when the generator doesn't accurately reflect the diversity of the target distribution. To address these gaps, the authors in [101] proposed Deep Convolutional Generative Adversarial Networks (DCGAN), which have a set of architectural constraints to balance GANs [117].

DCGAN is an extension of the GAN design that uses deep convolutional neural networks for both the generator and discriminator models, as well as configurations for the models and training that lead to stable training of a generator model. The DCGAN offers model constraints needed to construct high-quality generator models. This architecture enabled the rapid development of several GAN extensions and applications [99].

The architecture of GANs consists of two multi-layered networks, Generator and Discriminator. Both networks of a GAN are trained simultaneously [97]. The Generator network is tasked with creating synthetic images that closely resemble real images in the training dataset. On the other hand, the Discriminator network seeks to discriminate between real and fake images by evaluating the likelihood that a particular input sample is real or fake [98]. The two networks are trained so that the generator is encouraged to create images that the discriminator finds increasingly difficult to distinguish from real ones. Since GANs may be used to simulate the underlying data distribution, they form the basis of the proposed methodology. As a result, they get better at distinguishing various data sets with minimal alterations. As such, it produces synthetic data for each category to train the discriminator by employing a GAN to anticipate the data distribution. After training, the discriminator can accurately classify brand-new samples in each category, even when like examples in other classes [99].

Mathematically, to learn the generator's distribution $p_g$ over the image data represented as x, define the input noise vector $z$ with a prior distribution $p_z\left(z\right)$, then represent the data mapping to Generator $G\left(z;W_g\right)$ a neural network with parameters $W_g$. The discriminator $D\left(x;W_d\right)$ outputs a single scalar, where $D\left(x\right)$ is the probability that x comes from data rather than $p_g$. Discriminator D is trained to maximize the likelihood of correctly labelling training examples and G samples. At the same time, Generator G is trained to mislead the discriminator by attempting to minimize $\log \left(1-D\left(G\left(z\right)\right)\right)$. So, D and G play the following two-player minimax game with the value function V (G, D) [20] [97-99]:

$${min}_{G}m{ax}_{D}V\left(G,D\right)=E_{x~Pdata\left(x\right)}\left[\log \left(x\right)\right]+E_{x~Pz\left(z\right)}\left[\log \left(1-D\left(G\left(z\right)\right)\right)\right]$$

(1)

Theoretically, the discriminator makes a random guess as to whether the inputs are real or fake, and the solution to this minimax game is where $p_g=p_{data}$.

The primary goal of DCGAN is to extend the capabilities of GAN by making use of Convolution Network designs. Radford [101] achieved consistent outcomes by advocating for a few critical architectural limits on DCGAN. The authors of this study employed the basic ideas proposed by Radford [101] to classify skin lesions. Figure 4 and Figure 5 illustrate the flow diagram and improved simulation model used for the DCGAN framework. The following modifications are made to the Generator and Discriminator networks to avoid mode collapse, model instability and convergence [101]:

Modifications to the Generator:

• The generator uses five deconvolutional layers instead of four.
• Replace deterministic spatial pooling layers such as global average pooling with 2 x 2 fractional-stride convolutions (Generator), which allows the networks to learn by themselves spatial downsampling.
• Eliminate connected hidden layers to avoid model instability and stabilize the convergence speed.
• Update the generator weights using backpropagation and an optimizer SGDM with a constant learning rate of 0.01 instead of 0.0002.
• Batch normalization is used to stabilize the learning of the generator.
• All generating levels use the ReLu activation, except the output layer, which employs the Tanh activation to scale the output between -1 and 1.
• Modifications to Discriminator:
• The discriminator uses five convolutional- layers to train the networks instead of four.
• Replace deterministic spatial pooling layers such as max pooling with 2 x 2 stride convolutions (Discriminator), allowing the networks to learn spatial upsampling by themselves.
• Eliminate connected hidden layers to avoid model instability and stabilize the convergence speed.
• Update the weights of the discriminator using backpropagation and an optimization step.
• Batch normalization is used to stabilize the learning of the discriminator.
• The LeakyReLU activation function is used for all layers in the discriminator except the output layer to allow gradients to flow backwards through the layer.
• The final layer functions as a classifier and uses the SoftMax activation function for classification.

4.4.1. Model Training and Classification

The authors trained the proposed Improved DCGAN model on the ISIC 2017 dataset to classify the cutaneous skin lesion. Firstly, the dataset is split into train (70%) and test (30%) datasets. The training dataset is preprocessed and augmented and then passed into the proposed DCGAN model for training. A preprocessed random latent vector input is fed to the generator to generate synthetic data. The deconvolution neural network of our generator is activated by calling the transposedConv2dLayer function to perform weight multiplication and the Bias function to execute bias addition. In addition to performing weight multiplication and bias addition, the convolution neural network in the discriminator called the convolution2dLayer function performs weight multiplication. A transposed convolutional layer is typically used for upsampling or to generate an output feature map with a larger spatial dimension than the input feature map, thereby generating synthetic images. As shown in Figure 5, the authors built the generator to meet DCGAN framework requirements and set OUTPUT_SIZE to 64. The deconvolution step is 2, and each output is four times the input, so each layer's output size is 4 x 4, 8 x 8, 16 x 16, and 32 x 32, as well as feature maps are set to 512, 256, 128 and 64, respectively. The Generator images were rescaled to the range of [-1,1] for the tanh activation function. The model was trained with mini-batch size 64 using Stochastic Gradient Descent with Momentum (SGDM) optimizer with a learning rate set to 0.01 and momentum set to 0.5 to stabilize the training. The default values were initialized to zero for all the weights, and normal distribution, with a 0.02% standard deviation.

The discriminator is a feed-forward neural network with four convolutional neural networks. The LeakyReLU is set to 0.2. In addition to discriminating between three target classes, the discriminator also functions as a classifier. As a result, the discriminator output layer contains N + 1 (= 3) units, where n is the number of target classes. The final layer, D, is labelled for predicting the probability of an image being real or fake. The remaining N units in three layers are trained using a standard cross-entropy loss function which is represented as [101]:

$$L_{supervised}=-\sum p\left(D\left(x\right)\log \left(d\left(D\left(x\right)\right)\right)\right)$$

(2)

Whereas D(x) is the correct class label corresponding to the input x and $p\left(D\left(x\right)\right)$ is the probability of predicted class by discriminator D. Adding an extra label for the fake class allows us to train the discriminator network across two loss functions simultaneously: one for recognizing fake and genuine images and the other for classification inside actual images. The discriminator network backpropagates discriminator loss to update its weights.

The training step involves changing the network's weights based on the training data while minimizing the loss function, which measures the difference between the anticipated and real data. During the discriminator training phase, the generator is kept constant. Also, the discriminator is kept constant during the Generator training phase. As discriminator training seeks to determine how to differentiate between real and fake data, it learns how to identify the generator's defects. When it comes to discriminators, it is assumed that the real input needs to be close to 1 and that the result of the generators will be 0. For the generator network, the discriminator should predict 1 for each image it makes. During training, the validation data assesses the network's performance and prevent overfitting. Probability distributions are what GANs attempt to reproduce. Therefore, we employed loss functions that account for the discrepancy between GAN’s output and real distribution. The total loss for the discriminator is the sum of the losses for real and fake images [101]:

$$D\_loss=D\_real\_loss+D\_fake\_loss$$

(3)

G_loss is the cross entropy resulting from the difference between the generator's generated data and the discriminator's input data. In our study, the discriminator also serves as a classifier to distinguish between benign and malignant cancers. The mathematical description of a classifier with a training algorithm is shown below:

5. Experiments and Results

5.3. Results of Improved DCGAN-based Classifier

The ISIC 2017 dataset is used to train the proposed DCGAN-based Classifier. Image preprocessing techniques are applied before DCGAN modelling to ensure the model can learn reliable representations. As discussed in sub-section 4.4.1, 70% of the original datasets were used for training and 30% for testing each image class. Notably, 30% of the images were taken as unlabeled data. The adversarial network inputs random noise and provides the discriminator's final prediction on the created images. By altering the noise vector, we may thoroughly understand how the generator works and which noise vector results in the desired class. However, we have used 100 random latent input noises.

During the batch normalization, a mini-batch size of 64 was used. All weights were initialized with a normal distribution with a mean of zero and a standard deviation of 0.02. Both the generator and discriminator networks are composed of five layers. The generator comprises four deconvolutional layers with ReLu activations and one deconvolutional layer with tanh activation at the final layer. The discriminator consists of four convolutional layers with Leaky-ReLu activations and a SoftMax layer in the last layer. During the training process, both actual data and data from the generator network are fed to the discriminator. The generator produces extremely realistic images after a few epochs of training, which are also used for additional training. To verify the Improved DCGAN model's effectiveness, we randomly generated 100 images for each of the two skin lesions and compared them to discriminate between real and fake using the BAS evaluation metric; the sample images are shown in Figure 14 (b). Figure 14 (a) presents original images from the training data, and Figure 14 (b) shows the random synthetic images generated after training. It is observed that each image produced by DCGAN looks very real and is hard to differentiate from the real images.

Figure 14. (a): Real Images from the ISIC 2017 Dataset.

Figure 14. (a): Real Images from the ISIC 2017 Dataset.

Figure 14. (b). Synthetic Images Generated by Generator.

Figure 14. (b). Synthetic Images Generated by Generator.

Figure 15 shows the DCGAN model's training process, the score generated by the generator and discriminator after 383 epochs/ 9552 iterations. The score must be between 0 to 1; however, 0.5 is the best score for each iteration [102]. After each cycle, training losses for generator G and discriminator D are recorded. The generator should get massive random noise early in training to learn how to generate real data. At the same time, the discriminator can distinguish real from fake images; it does not usually collect large samples early on. The generator and discriminator may overcome each other during training. If the discriminator gets too accurate, it will return values close to 0 or 1. If the image generator method is used for producing and classifying skin lesions during training, GAN becomes too accurate; it will constantly harness discriminator mistakes, producing unpleasant effects. In our experiment, the discriminator’s score is close to 0.5, indicating that the generator creates a synthetic image that makes it impossible for the discriminator to differentiate whether it is real or fake.

The generator and discriminator networks were trained for ten epochs to examine the validation accuracy of the model with definite learning rates of 0.01, 0.001, and 0.0002. Seventeen iterations per epoch were taken during the training phase, while a validation frequency of 50 iterations was used for testing. Accuracy and loss during training for the generator and discriminator are depicted in Figure 16, Figure 17 and Figure 18 for ten epochs. The loss convergence at the end demonstrates that the GAN model has found its optimal state. Compared to the learning rates of 0.001 and 0.0002, the proposed DCGAN model achieves an optimal accuracy of 99.38% with a learning rate of 0.01 which outperforms state-of-the-art methods of GAN models as suggested in the literature, depicted in Figure 16, Figure 17 and Figure 18. The images were trained using a single GPU to accelerate the matrix operations, training and for faster and parallel computation. Table 5 shows the time elapsed, validation accuracy and loss of three learning rates computed for ten epochs with a batch size of 64. In modern deep-learning models, batch size is one of the most important hyperparameters to fine-tune the model performance. To enable the model to detect the pattern in the data without having to train on a huge dataset, the authors additionally evaluated the model on 128 and 256 batch sizes with a learning rate of 0.01, which gives higher accuracy than 0.001 and 0.0002. Table 6 demonstrates that the training time for a model is substantially reduced as the batch size increases.

A confusion plot is a graphical representation of a classification model's performance. It calculates AUC-ROC, accuracy, precision, recall, and F1 score metrics to evaluate model performance. As shown in Figure 19, the discriminator successfully diagnoses benign and malignant skin lesions with an extremely high degree of accuracy, demonstrated in the diagonal elements of the metrics. The confusion matrix for three learning rates was compared in Figure 19.

The ROC curve in Figure 20 summarises and displays the binary classification results, particularly for the positive class, and has learning rates of 0.01, 0.001, and 0.0002, respectively. We used True Positive (TP) and False Positive (FP) to plot the ROC curve, as was mentioned in Section 5.1. TP is on the y-axis, and FP is on the x-axis in the ROC plot. The plot's grey line is a random classifier's ROC curve. The trade-off between sensitivity (TPR) and specificity (1-FPR) is depicted by our ROC curve (Figure 20). Good classifiers perform better if their curves are located nearer the top-left corner. A curve close to the ROC space's 45-degree diagonal indicates a less accurate test. Despite the ROC curve's deviation from the diagonal and the little gap between the top left corner and the curve, Figure 20 demonstrates that the Classifier is fairly classified. As shown in Figure 20, data1 is a linear line, and data2 is the ROC curve, a quadratic pattern. R-squared (R² )for all three learning rates equal 1, indicating that the predicted values are identical to the actual values.

The performance analysis of the improved DCGAN model utilizing the performance metrics BAS, accuracy, recall, precision, specificity, and F1 Score is shown in Table 6 and Figure 21 for three definite learning rates of 0.01, 0.001, and 0.0002. The model produces good results after 170 epochs, with the test loss reaching a minimal state and the resulting BAS being 99 for learning rates 0.01 and 0.001, and 97 for 0.0002, indicating that the classifier performance is significantly good. The accuracy with a learning rate of 0.01 obtains a greater accuracy of 99.38% compared to a 0.001 model's accuracy of 98.44% and a 0.0002 model's accuracy of 96.04%. As a result, the findings indicate that the proposed model could generalize well and operate well when applied to any skin lesions.

6. Conclusion and Future Scope

7. Patents

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