Chest radiography (CXR) is a popular imaging modality for screening lung abnormalities. It plays a vital role in monitoring disease progression, allocating limited medical resources effectively, and planning treatment in clinical practice. However, manual CXR image screening is time-consuming and susceptible to human errors. Therefore, computer-aided diagnosis (CAD) systems can be an additional decision-making tool for radiologists. Although prior research had remarkable success in COVID-19 classification tasks, it is still difficult to accurately localize the infection and quantify its severity, which restricts the generalizability and interpretability of CAD-based systems. This work proposes an end-to-end multi-stage architecture for COVID-19 classification, infection region identification, and disease severity assessment. The first stage of the proposed architecture identifies the relevant lung regions by eliminating the irrelevant portions. The second stage discriminates COVID-19 from pneumonia and normal images. In this study, instead of a single pre-trained model, we used three models and a fuzzy rank-based ensemble method to combine the results. Unlike the simple ensemble techniques, the proposed approach generates the final predictions for the test samples by evaluating the confidence in the predictions of the base models. In the final stage of the network, the infection regions are identified, and the amount of infection is calculated when the image is classified as COVID-19. Subsequently, a severity score ranging from 0 to 3 is assigned based on the level of infection severity. Based on the predicted scores, COVID-19 images are categorized into four severity levels: mild, moderate, severe, and critical. The final ensemble segmentation model exceeds all the individual segmentation models with an IoU score of 98.99% and a dice score of 99.49%. Additionally, the infection segmentation model also outperforms all other segmentation models individually, achieving a dice score of 96.75% and an IoU score of 93.71%. Our proposed segmentation-based classification model achieves a maximum accuracy of 98.05% with an F1-score of 97.76% and outperforms five state-of-the-art (SOTA) CNN models and five existing ensemble techniques in the literature. An external dataset also validated the model, and the results showed an accuracy of 97.63% and an F1-score of 97.22%. 

A Multi-stage framework for COVID-19 detection and severity assessment from chest radiography images using advanced fuzzy ensemble technique

Simple Summary Artificial intelligence (AI) has shown promise in detecting and diagnosing nonmelanoma skin cancer through image analysis. The incidence of skin cancer continues to rise each year, and it is estimated that one in five Americans will have nonmelanoma skin cancer at some point in their lifetime. Non-invasive diagnostic tools are becoming more widely adopted as the standard of care. When integrated with AI, there is the potential to identify skin cancer earlier and more rapidly compared to traditional methods. This review aims to assess the current status of AI diagnostic algorithms in tandem with noninvasive imaging for the detection of nonmelanoma skin cancer. Abstract Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.

Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review

The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies are improved. Currently, diagnosis is heavily based on physicians’ judgment and experience, which can occasionally lead to the worsening of the lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy or multiphoton laser scanning microscopy, have been explored for skin cancer assessment, which have been aligned with different artificial intelligence (AI) strategies to assist in the diagnostic task, based on several image features, thus making the process more reliable and faster. This systematic review concerns the implementation of AI methods for skin tumor classification with different imaging modalities, following the PRISMA guidelines. In total, 206 records were retrieved and qualitatively analyzed. Diagnostic potential was found for several techniques, particularly for dermoscopy images, with strategies yielding classification results close to perfection. Learning approaches based on support vector machines and artificial neural networks seem to be preferred, with a recent focus on convolutional neural networks. Still, detailed descriptions of training/testing conditions are lacking in some reports, hampering reproduction. The use of AI methods in skin cancer diagnosis is an expanding field, with future work aiming to construct optimal learning approaches and strategies. Ultimately, early detection could be optimized, improving patient outcomes, even in areas where healthcare is scarce. 

Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review

Skin cancer detection has become a complex task for medical professionals worldwide. Early diagnosis of carcinogens is important for treating skin cancer and reducing mortality rates, but it still remains a daunting task The models available for effective diagnosis of patients provide less accuracy due to data imbalance. Presently, Generative Adversarial Networks are being utilized but the issue of variations within and between different categories of images is still prevalent. This problem arises due to the shortage of data. This study proposes the use of Progressive Growing of Generative Adversarial Network that aims at producing realistic synthetic images using PAD-UFES 20 dataset that consists of non-dermoscopic images. It is further integrated with classifiers based on convolutional networks for better results. This way model over-fitting and data imbalance is prevented, and diagnosis accuracy is enhanced. This study further aims at producing images with better resolution as well as increasing the classification accuracy between cancerous and non-cancerous skin lesions. 

Skin cancer classification using Progressive Growing of Generative Adversarial Network

Accurate medical diagnosis is essential for informed decision making and the delivery of effective treatment. Traditionally, this process relies on clinical judgment, integrating data and medical expertise to inform decision making. In recent years, artificial neural networks (ANNs) have proven to be valuable tools for diagnostic support. Attention mechanisms have enhanced ANNs performance, while fuzzy logic has contributed to managing uncertainty inherent in clinical data. This systematic review analyzes how the integration of these three approaches enhances computational models for medical diagnostic support. Following PRISMA 2020 guidelines, a comprehensive search was conducted across five scientific databases (IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and ACM Digital Library) for studies published between 2020 and 2025 that implemented the combined use of ANNs, attention mechanisms, and fuzzy logic for medical diagnostic support. Inclusion and exclusion criteria were applied, along with a quality assessment. Data extraction and synthesis were conducted independently by two reviewers and verified by a third. Out of 269 initially identified articles, 32 met the inclusion criteria. The findings consistently indicate that the integration of ANNs, attention mechanisms, and fuzzy logic significantly improves the performance of diagnostic models. ANNs effectively capture complex data patterns, attention mechanisms prioritize the most relevant features, and fuzzy logic provides robust handling of ambiguity and imprecise information through continuous degrees of membership. This integration leads to more accurate and interpretable diagnostic models. Future research should focus on leveraging multimodal data, enhancing model generalization, reducing computational complexity, and exploring novel fuzzy logic techniques and training paradigms to improve adaptability in real-world clinical settings. 

Artificial Neural Network, Attention Mechanism and Fuzzy Logic-Based Approaches for Medical Diagnostic Support: A Systematic Review

Background and Objective. Skin lesion classification by using deep learning technologies is still a considerable challenge due to high similarity among classes and large intraclass differences, serious class imbalance in data, and poor classification accuracy with low robustness. Approach. To address these issues, a two-stage framework for dermoscopy lesion classification using adversarial training and a fuzzy rank-based ensemble of multilayer feature fusion convolutional neural network (CNN) models is proposed. In the first stage, dermoscopy dataset augmentation based on generative adversarial networks is proposed to obtain realistic dermoscopy lesion images, enabling significant improvement for balancing the number of lesions in each class. In the second stage, a fuzzy rank-based ensemble of multilayer feature fusion CNN models is proposed to classify skin lesions. In addition, an efficient channel integrating spatial attention module, in which a novel dilated pyramid pooling structure is designed to extract multiscale features from an enlarged receptive field and filter meaningful information of the initial features. Combining the cross-entropy loss function with the focal loss function, a novel united loss function is designed to reduce the intraclass sample distance and to focus on difficult and error-prone samples to improve the recognition accuracy of the proposed model. Main results. In this paper, the common dataset (HAM10000) is selected to conduct simulation experiments to evaluate and verify the effectiveness of the proposed method. The subjective and objective experimental results demonstrate that the proposed method is superior over the state-of-the-art methods for skin lesion classification due to its higher accuracy, specificity and robustness. Significance. The proposed method effectively improves the classification performance of the model for skin diseases, which will help doctors make accurate and efficient diagnoses, reduce the incidence rate and improve the survival rates of patients.

Dermoscopy lesion classification based on GANs and a fuzzy rank-based ensemble of CNN models

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Dermoscopy lesion classification based on GANs and a fuzzy rank-based ensemble of CNN models