Generative Adversarial Network Image synthesis method for skin lesion Generation and classification

Skin cancer is the most commonly diagnosed cancer in today's growing population. One of the common limitations in the treatment of cancer is in the early detection of this disease. Mostly, skin cancer is detected in its later stages, when it has already compromised most of the skin area. Early detection of skin cancer is of utmost importance in increasing the chances for successful treatment, thus reducing mortality and morbidity. Currently, most dermatologists use a special microscope to examine the pattern and the affected area. This method is time-consuming and is prone to human errors, so there is a need for detecting skin cancer automatically. In this study, we investigate the automated classification of skin cancer using the Deep Convolution Generative Adversarial Network(DCGAN).In this work, Deep Convolutional GAN is used to generate realistic synthetic dermoscopic images, in a way that could enhance the classification performance in a large dataset and to evaluate whether the classification accuracy is enhanced or not, by generating a substantial amount of new skin lesion images. The DCGAN is trained using images generated by the Generator and then tweaked using the actual images and allow the Discriminator to make a distinction between fake and real images. The DCGAN might need slightly more fine-tuning to ripe a better return. Hyperparameter optimization can be utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters, namely number of iterations, batch size, and Learning rate can be tweaked, for example in this work we decreased the learning rate from the default 0.001 to 0.0002 and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models. Moreover, at each iteration in the course of the training process, the weights of the discriminative and generative network are updated to balance the loss between them. This pretraining and fine-tuning process is substantial for the model performance

Towards equitable deep learning in dermatology: assessing lesion classification fairness across skin tones

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Oliver Díaz i Richard OsualaRecent advances in deep learning skin lesion classifiers rose expectations that these models can be implemented in the clinical routine in the near future. However, before deploying deep learning models in such a sensitive area as healthcare, it is important to ensure their trustworthiness and mitigate any kind of discrimination. This thesis investigates discrimination by skin tone in a light-weight deep learning skin lesion classifier trained on a benchmark dataset of dermatological images and assesses the feasibility of SinGAN-generated synthetic dark skin images to improve predictions on dark skin samples in the absence of dark skin training data. The results suggest that (I) there is discrimination by skin tone, (II) a data shift from apparent light skin samples in training to apparent dark skin samples in deployment deteriorates predictions, and (III) although dark SinGAN-generated samples may improve performance, oversampling of a few dark skin samples appears more feasible. Most importantly, however, a thorough analysis of automated skin tone estimations with the Individual Topology Angle revealed that (IV) these skin tone estimations might measure the darkness of a skin image rather than the darkness of skin in the image and (V) the investigated HAM10000 dataset is less diverse than previous research suggested. This has potentially wide-ranging implications for previous publications about skin tone fairness using this dataset and emphasizes the need for further research on more diverse dermatology datasets with more reliable skin tone labels before wide-spread deployment of skin lesion classifiers

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements