Skin disease diagnosis with deep learning: A review

A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases

Skin diseases are a significant global public health concern, affecting 21-85% of the world's population, particularly those in low- and middle-income countries. Accurate and timely diagnosis is crucial for effective treatment and improved patient outcomes. This study introduces a novel deep-learning multi-model architecture designed for high-precision skin disease diagnosis. The system employs a five-category Xception model to classify skin lesions into five classes: Atopic Dermatitis, Acne and Rosacea, Skin Cancer, Bullous, and Others. Trained on 25,010 images, the model achieved 95% accuracy and an AUROC of 99.4%. To further enhance accuracy, transfer learning was applied, resulting in specialized models for each class, with strong performance across 40 skin conditions. Specifically, the Acne and Rosacea model achieved an accuracy of 90.0%, with a precision of 90.7%, recall of 90.1%, f1-score of 90.2%, and an AUROC of 99.0%. The Skin Cancer model demonstrated 94.0% accuracy, 94.8% precision, 94.2% recall, 94.1% f1-score, and a 99.5% AUROC. The Atopic Dermatitis model reported 91.8% accuracy, 92.2% precision, 91.8% recall, 91.9% f1-score, and a 98.8% AUROC. Finally, the Bullous model showed 90.0% accuracy, 90.6% precision, 90.0% recall, 90.0% f1-score, and a 98.9% AUROC. This approach surpasses previous studies, offering a more comprehensive diagnostic tool for skin diseases. To facilitate result reproducibility, the training and testing codes for the models utilized in this study are accessible via the GitHub repository ( https://github.com/SaraEl-Metwally/A-Multi-Model-Deep-Learning-for-Diagnosing-Skin-Diseases ).

Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability

BACKGROUND

Cutaneous melanoma is the most aggressive form of skin cancer, responsible for most skin cancer-related deaths. Recent advances in artificial intelligence, jointly with the availability of public dermoscopy image datasets, have allowed to assist dermatologists in melanoma identification. While image feature extraction holds potential for melanoma detection, it often leads to high-dimensional data. Furthermore, most image datasets present the class imbalance problem, where a few classes have numerous samples, whereas others are under-represented.

METHODS

In this paper, we propose to combine ensemble feature selection (FS) methods and data augmentation with the conditional tabular generative adversarial networks (CTGAN) to enhance melanoma identification in imbalanced datasets. We employed dermoscopy images from two public datasets, PH2 and Derm7pt, which contain melanoma and not-melanoma lesions. To capture intrinsic information from skin lesions, we conduct two feature extraction (FE) approaches, including handcrafted and embedding features. For the former, color, geometric and first-, second-, and higher-order texture features were extracted, whereas for the latter, embeddings were obtained using ResNet-based models. To alleviate the high-dimensionality in the FE, ensemble FS with filter methods were used and evaluated. For data augmentation, we conducted a progressive analysis of the imbalance ratio (IR), related to the amount of synthetic samples created, and evaluated the impact on the predictive results. To gain interpretability on predictive models, we used SHAP, bootstrap resampling statistical tests and UMAP visualizations.

RESULTS

The combination of ensemble FS, CTGAN, and linear models achieved the best predictive results, achieving AUCROC values of 87% (with support vector machine and IR=0.9) and 76% (with LASSO and IR=1.0) for the PH2 and Derm7pt, respectively. We also identified that melanoma lesions were mainly characterized by features related to color, while not-melanoma lesions were characterized by texture features.

CONCLUSIONS

Our results demonstrate the effectiveness of ensemble FS and synthetic data in the development of models that accurately identify melanoma. This research advances skin lesion analysis, contributing to both melanoma detection and the interpretation of main features for its identification.

MobileNet-V2: An Enhanced Skin Disease Classification by Attention and Multi-Scale Features

The increasing prevalence of skin diseases necessitates accurate and efficient diagnostic tools. This research introduces a novel skin disease classification model leveraging advanced deep learning techniques. The proposed architecture combines the MobileNet-V2 backbone, Squeeze-and-Excitation (SE) blocks, Atrous Spatial Pyramid Pooling (ASPP), and a Channel Attention Mechanism. The model was trained on four diverse datasets such as PH2 dataset, Skin Cancer MNIST: HAM10000 dataset, DermNet. dataset, and Skin Cancer ISIC dataset. Data preprocessing techniques, including image resizing, and normalization, played a crucial role in optimizing model performance. In this paper, the MobileNet-V2 backbone is implemented to extract hierarchical features from the preprocessed dermoscopic images. The multi-scale contextual information is fused by the ASPP model for generating a feature map. The attention mechanisms contributed significantly, enhancing the extraction ability of inter-channel relationships and multi-scale contextual information for enhancing the discriminative power of the features. Finally, the output feature map is converted into probability distribution through the softmax function. The proposed model outperformed several baseline models, including traditional machine learning approaches, emphasizing its superiority in skin disease classification with 98.6% overall accuracy. Its competitive performance with state-of-the-art methods positions it as a valuable tool for assisting dermatologists in early classification. The study also identified limitations and suggested avenues for future research, emphasizing the model's potential for practical implementation in the field of dermatology.

DermSynth3D: Synthesis of in-the-wild annotated dermatology images

In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis. However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions, insufficient annotations, and non-standardized image acquisitions. To address these shortcomings, we propose a novel framework called DermSynth3D. DermSynth3D blends skin disease patterns onto 3D textured meshes of human subjects using a differentiable renderer and generates 2D images from various camera viewpoints under chosen lighting conditions in diverse background scenes. Our method adheres to top-down rules that constrain the blending and rendering process to create 2D images with skin conditions that mimic in-the-wild acquisitions, ensuring more meaningful results. The framework generates photo-realistic 2D dermatological images and the corresponding dense annotations for semantic segmentation of the skin, skin conditions, body parts, bounding boxes around lesions, depth maps, and other 3D scene parameters, such as camera position and lighting conditions. DermSynth3D allows for the creation of custom datasets for various dermatology tasks. We demonstrate the effectiveness of data generated using DermSynth3D by training DL models on synthetic data and evaluating them on various dermatology tasks using real 2D dermatological images. We make our code publicly available at https://github.com/sfu-mial/DermSynth3D.

Advancing healthcare: the role and impact of AI and foundation models

BACKGROUND

The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems.

PURPOSE

This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes.

RESULTS

This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models.

CONCLUSION

The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.

Developing an efficient method for melanoma detection using CNN techniques

BACKGROUND

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically deadly. Any bodily part may become infected by cancerous cells, which can be fatal. Skin cancer is one of the most prevalent types of cancer, and its prevalence is rising across the globe. Squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and causes the majority of deaths, are the primary subtypes of skin cancer. Screening for skin cancer is therefore essential.

METHODS

The best way to quickly and precisely detect skin cancer is by using deep learning techniques. In this research deep learning techniques like MobileNetv2 and Dense net will be used for detecting or identifying two main kinds of tumors malignant and benign. For this research HAM10000 dataset is considered. This dataset consists of 10,000 skin lesion images and the disease comprises nonmelanocytic and melanocytic tumors. These two techniques can be used for detecting the malignant and benign. All these methods are compared and then a result can be inferred from their performance.

RESULTS

After the model evaluation, the accuracy for the MobileNetV2 was 85% and customized CNN was 95%. A web application has been developed with the Python framework that provides a graphical user interface with the best-trained model. The graphical user interface allows the user to enter the patient details and upload the lesion image. The image will be classified with the appropriate trained model which can predict whether the uploaded image is cancerous or non-cancerous. This web application also displays the percentage of cancer affected.

CONCLUSION

As per the comparisons between the two techniques customized CNN gives higher accuracy for the detection of melanoma.

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin cancer classification, ensemble methods offer a pathway for further enhancing diagnostic accuracy. This study introduces a cutting-edge approach employing the Max Voting Ensemble Technique for robust skin cancer classification on ISIC 2018: Task 1-2 dataset. We incorporate a range of cutting-edge, pre-trained deep neural networks, including MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, and Xception. These models have been extensively trained on skin cancer datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages the synergistic capabilities of these models by combining their complementary features to elevate classification performance further. In our approach, input images undergo preprocessing for model compatibility. The ensemble integrates the pre-trained models with their architectures and weights preserved. For each skin lesion image under examination, every model produces a prediction. These are subsequently aggregated using the max voting ensemble technique to yield the final classification, with the majority-voted class serving as the conclusive prediction. Through comprehensive testing on a diverse dataset, our ensemble outperformed individual models, attaining an accuracy of 93.18% and an AUC score of 0.9320, thus demonstrating superior diagnostic reliability and accuracy. We evaluated the effectiveness of our proposed method on the HAM10000 dataset to ensure its generalizability. Our ensemble method delivers a robust, reliable, and effective tool for the classification of skin cancer. By utilizing the power of advanced deep neural networks, we aim to assist healthcare professionals in achieving timely and accurate diagnoses, ultimately reducing mortality rates and enhancing patient outcomes.

Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward

Artificial Intelligence (AI) has emerged as a transformative force in the field of diagnostic dermatology, offering unprecedented capabilities in image recognition and data analysis. Despite its promise, the integration of AI into clinical practice faces multifaceted challenges that span technical, ethical, and regulatory domains. This article provides a narrative overview of the current state of AI in dermatology, tracing its historical evolution from early diagnostic tools to contemporary hybrid supervised models. We identify and categorize six critical challenges: data quality and quantity, algorithmic development and explainability, ethical considerations, clinical workflow integration, regulatory frameworks, and stakeholder collaboration. Each challenge is dissected from the perspectives of academia, industry, and healthcare providers, offering actionable recommendations for future research and implementation. We also highlight the paradigm shift in AI research, emphasizing the potential of transformer architectures in revolutionizing diagnostic methodologies. By addressing the challenges and harnessing the latest advancements, AI has the potential to significantly impact diagnostic accuracy and patient outcomes in dermatology.

Automatic Segmentation of Psoriasis Skin Images Using Adaptive Chimp Optimization Algorithm-Based CNN

Psoriasis is a severe skin disease that is surveyed outwardly by dermatologists. In recent years, computer vision is the major solution for diagnosing the psoriasis skin disease by segmenting the infected skin images. Besides, many researchers had presented efficient machine learning techniques for segmenting the psoriasis skin images. Nevertheless, accuracy and time consumption of the model are further to be improved. Thus, in this work, we present adaptive chimp optimization algorithm (AChOA)-based convolutional neural network (CNN) which is introduced for automatic segmentation of psoriasis skin images. After pre-processing, the input images are segmented using AChOA-CNN model where weight and bias values of CNN are optimized with the AChOA. The search ability of ChOA is enhanced by adapting the chaotic sequence based on tent map. At final, from the segmented output images, artifacts are removed by applying the threshold module. From the simulation, we attain 97% of accuracy.

AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions

Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.

Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification

Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family.

Acne Detection by Ensemble Neural Networks

Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were introduced in acne detection to improve detection precision. However, it remains challenging to diagnose acne based on the facial images of patients due to the complex context and special application scenarios. Here, we provide an ensemble neural network composed of two modules: (1) a classification module aiming to calculate the acne severity and number; (2) a localization module aiming to calculate the detection boxes. This ensemble model could precisely predict the acne severity, number, and position simultaneously, and could be an effective tool to help the patient self-test and assist the doctor in the diagnosis.

Skin Cancer Classification With Deep Learning: A Systematic Review

Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model's cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers' convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.

Biomimetic Nanoscale Materials for Skin Cancer Therapy and Detection

Skin cancer has developed as one of the most common types of cancer in the world, with a significant impact on public health impact and the economy. Nanotechnology methods for cancer treatment are appealing since they allow for the effective transport of medicines and other biologically active substances to specific tissues while minimizing harmful consequences. It is one of the most significant fields of research for treating skin cancer. Various nanomaterials have been employed in skin cancer therapy. The current review will summarize numerous methods of treating and diagnosing skin cancer in the earliest stages. There are numerous skin cancer indicators available for the prompt diagnosis of this type of disease. Traditional approaches to skin cancer diagnosis are explored, as are their shortcomings. Electrochemical and optical biosensors for skin cancer diagnosis and management were also discussed. Finally, various difficulties concerning the cost and ease of use of innovative methods should be addressed and overcome.

System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of Heterogeneous Data Based on a Multimodal Neural Network

Simple Summary

Skin cancer is one of the most common cancers in humans. This study aims to create a system for recognizing pigmented skin lesions by analyzing heterogeneous data based on a multimodal neural network. Fusing patient statistics and multidimensional visual data allows for finding additional links between dermoscopic images and medical diagnostic results, significantly improving neural network classification accuracy. The use by specialists of the proposed system of neural network recognition of pigmented skin lesions will enhance the efficiency of diagnosis compared to visual diagnostic methods.

Abstract

Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.