Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

Abstract

The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies like dermoscopy, an imaging device that detects skin lesions early in the disease, are a driving factor. The lack of annotated data and class-imbalance datasets makes using automated diagnostic methods challenging for skin lesion classification. In recent years, deep learning models have performed well in medical diagnosis. Unfortunately, such models require a substantial amount of annotated data for training. Applying a data augmentation method based on generative adversarial networks (GANs) to classify skin lesions is a plausible solution by generating synthetic images to address the problem. This article proposes a skin lesion synthesis and classification model based on an Improved Deep Convolutional Generative Adversarial Network (DCGAN). The proposed system generates realistic images using several convolutional neural networks, making training easier. Scaling, normalization, sharpening, color transformation, and median filters enhance image details during training. The proposed model uses generator and discriminator networks, global average pooling with 2 × 2 fractional-stride, backpropagation with a constant learning rate of 0.01 instead of 0.0002, and the most effective hyperparameters for optimization to efficiently generate high-quality synthetic skin lesion images. As for the classification, the final layer of the Discriminator is labeled as a classifier for predicting the target class. This study deals with a binary classification predicting two classes—benign and malignant—in the ISIC2017 dataset: accuracy, recall, precision, and F1-score model classification performance. BAS measures classifier accuracy on imbalanced datasets. The DCGAN Classifier model demonstrated superior performance with a notable accuracy of 99.38% and 99% for recall, precision, F1 score, and BAS, outperforming the state-of-the-art deep learning models. These results show that the DCGAN Classifier can generate high-quality skin lesion images and accurately classify them, making it a promising tool for deep learning-based medical image analysis.

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

“…Each phase of the proposed model is evaluated using qualitative and procedural methods. The performance metrics [ 91 ], such as accuracy, recall, sensitivity, specificity, and AUC-ROC, were used to evaluate the effectiveness of this study’s skin lesion classification model. In this study, all the experimental tests were implemented using IDLE Shell 3.11.4 on a 4 GHz Intel Core i7 CPU at a rate of @ 1.80 GHz, 2304 Mhz, 4 Core(s), 8 Logical Processor(s), 12 GB of NVIDIA K80 GPU RAM, and 4.1 TFLOPS of performance.…”

“…Deterministic gradient descent is a version of SGD without gradient noise. This broadside emphasizes stochastic optimization, but its principles and methods apply to deterministic gradient descent [ 91 ]. In this context, we focused on minimizing the cost function.…”

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

“…Recent studies have utilized various types of classification and preliminary processing methods to conduct morphological change examines on grey-level skin cancer images. These images were obtained from the PH2 repository and were subjected to classification and clustering procedures using a pre-trained Levenberg-Mean neural network 6 . Transfer learning has been utilized to forecast skin cancer visuals from the HAM10000 database precisely using the MobileNet CNN 19 .…”

“…Additionally, it is possible to utilize pre-trained deep learning models as feature extractors without additional training, provided they have already been trained on tasks or domains that are similar or related 6 . The deep learning model may acquire features that contain noise, thereby impacting the accuracy of the final classification.…”

Maize Leaf Compound Disease Recognition Based on Attention Mechanism

“…The CycleGAN [21,22] is a variant of the generative adversarial network (GAN) [23][24][25] and offers several advantages over traditional GANs. Firstly, the CycleGAN can learn the mapping relationship between two different domains even without paired data.…”