Skin Lesion Classification Research Articles

A Dynamic Multi‐Output Convolutional Neural Network for Skin Lesion Classification

ABSTRACTSkin cancer is a pressing global health issue, with high incidence and mortality rates. Convolutional neural network (CNN) models have been proven to be effective in improving performance in skin lesion image classification and reducing the medical burden. However, the inherent class imbalance in training data caused by the difficulty of collecting dermoscopy images leads to categorical overfitting, which still limits the effectiveness of these data‐driven models in recognizing few‐shot categories. To address this challenge, we propose a dynamic multi‐output convolutional neural network (DMO‐CNN) model that incorporates exit nodes into the standard CNN structure and includes feature refinement layers (FRLs) and an adaptive output scheduling (AOS) module. This model improves feature representation ability through multi‐scale sub‐feature maps and reduces the inter‐layer dependencies during backpropagation. The FRLs ensure efficient and low‐loss down‐sampling, while the AOS module utilizes a trainable layer selection mechanism to refocus the model's attention on few‐shot lesion categories. Additionally, a novel correction factor loss is introduced to supervise and promote AOS convergence. Our evaluation of the DMO‐CNN model on the HAM10000 dataset demonstrates its effectiveness in multi‐class skin lesion classification and its superior performance in recognizing few‐shot categories. Despite utilizing a very simple VGG structure as the sole backbone structure, DMO‐CNN achieved impressive performance of 0.885 in BACC and 0.983 in weighted AUC. These results are comparable to those of the ensemble model that won the ISIC 2018 challenge, highlighting the strong potential of DMO‐CNN in dealing with few‐shot skin lesion data.

Predicting psoriasis severity using machine learning: A systematic review.

In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic reviews exists to explore the role of ML in predicting the severity of psoriasis. This systematic review aims to identify and summarize the existing literature on predicting psoriasis severity using ML algorithms and identify gaps in current clinical applications of these tools. OVID Embase, OVID MEDLINE, ACM Digital Library, Scopus, and IEEE Xplore were searched from inception to August, 2024. A total of 30 articles met our inclusion criteria and were included in this review. One article used serum biomarkers, while the remaining 29 used image-based models. The most common severity assessment score employed by these ML models was the Psoriasis Area Severity Index score, followed by Body Surface Area, with fifteen and five articles, respectively. The small size and heterogeneity of the existing literature are the primary limitations of this review. Progress in assessing skin lesion severity through ML in dermatology has advanced, but prospective clinical applications remain limited. ML and AI promise to improve psoriasis management, especially in non-image-based applications requiring further exploration. Large-scale prospective trials using diverse image datasets are necessary to evaluate and predict the clinical value of these predictive AI models.

Accurate Deep Learning Algorithms for Skin Lesion Classification

Accurate Deep Learning Algorithms for Skin Lesion Classification

A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity

The recent ‘Mpox’ outbreak, formerly known as ‘Monkeypox’, has been a significant public health concern and spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods excel at learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the “Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)” as a follow-up to the previously released open-access dataset, one of the first to contain mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. We leverage transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images. To reduce the impact of racial bias, we employ a color space data augmentation method to increase skin color variability during training and present a rigorous assessment of the technique’s effectiveness in skin lesion classification; we achieved the best overall accuracy of 83.59±2.11%. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient. We believe that the presented tool can be of significant impact in screening of mpox in case of a future outbreak.

SILP: Enhancing skin lesion classification with spatial interaction and local perception

Because of the harmful effects of ultraviolet rays and global environmental factors, the number of patients with skin lesions is increasing. If left untreated, skin lesions may lead to skin cancer. However, limited access to specialized medical care remains a challenge in certain regions. Therefore, there is an urgent need for an efficient, accurate, and accessible tool to identify suspicious lesions. Although there are many classification models for skin lesions, there is still room for improvement in terms of accuracy. To enhance the accuracy of skin lesion classification, a novel system named SILP is proposed in this study. There are two modules in SILP: the LPM and the SIM. Additionally, we have changed the activation function from GELU to SiLU to improve both training time and accuracy. SILP, along with several other models, has been tested on two public skin lesion datasets. The results demonstrate that our proposed system outperforms the state-of-the-art skin lesion classification model, not only in terms of accuracy but also in various other evaluation metrics.

Disagreement serves an explainable ensemble model based on Dempster–Shafer evidence-fusion for an improved skin lesion classification

The ever-growing development of deep learning models for skin lesion classification yields consistently to exceptional performance. These models revealed remarkable accuracy and robustness in the diagnosis of various skin lesion classes. Although this advancement enables significant aids in early detection and diagnosis, selecting the most suitable model for a task is challenging especially when most models demonstrate high proficiency. Consequently, small performance variations among well-performing models become critical factors. These variations present an additional challenge especially when considering the black-box nature of the model, which may result in a lack of evidence and certainty when making critical decisions. In this study, we tackle the problem of selecting the most appropriate models for skin lesion classification among various well-performing, yet unexplainable deep learning models. In fact, by proposing an ensemble approach that leverages Dempster–Shafer theory and feature importance visualization, we aim to create a trustworthy and interpretable model for improved skin lesion classification. The proposed Dempster–Shafer evidence-fusion ensemble model, provides insights into evidence-based integration of multiple models allowing for enhanced trustworthiness in the decision-making process. This is accomplished through comparing the high performances between different deep learning models. Hence, metrics are extracted by performing statistical tests and explainable methods besides numerical assessment, from the failure cases to profile the disagreement from different perspectives. With these metrics, the classifiers’ complementary behaviors is then identified, accordingly making contribution in proposing an explainable ensemble model and leveraging the complementary strengths of the individual models. The proposed ensemble model has been exhaustively evaluated using the challenging HAM10000 dataset. The results obtained indicate that the proposed skin lesion classifier can provide useful decision support while significantly improving classification performance by 6% in terms of accuracy compared with the best-performing individual model, and achieving 92% accuracy.

Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach.

Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8222 files selected from the authors' collection and the ISIC 2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy in four categories-melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi-using an average of 1600 images per category. Adding two categories with fewer images (about 700 each)-squamous cell carcinoma and actinic keratoses-reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements.

GAN based augmentation using a hybrid loss function for dermoscopy images

Dermatology is the most appropriate field to utilize pattern recognition-based automated techniques for objective, accurate, and rapid diagnosis because diagnosis mainly relies on visual examinations of skin lesions. Recent approaches utilizing deep learning techniques have shown remarkable results in this field. However, they necessitate a substantial quantity of images and the availability of dermoscopy images is often limited. Also, even if enough images are available, their labeling requires expert knowledge and is time-consuming. To overcome these issues, an efficient augmentation approach is needed to expand training datasets from input images. Therefore, in this work, a generative adversarial network has been developed using a new hybrid loss function constructed with traditional loss functions to enhance the generation power of the architecture. Also, the effect of the proposed approach and different generative network-based augmentations, which have been used with dermoscopy images in the literature, on the classification of skin lesions has been investigated. Therefore, the main contributions of this work are: (i) introducing a new generative model for the augmentation of dermoscopy images; (ii) presenting the effect of the proposed model on the classification of the images; (iii) comparative evaluations of the effectiveness of different generative network-based augmentations in the classification of seven forms of skin lesions. The classification accuracy when the proposed augmentation is used is 93.12%, which is higher than its counterparts. Experimental results indicate the significance of augmentation techniques in the classification of skin lesions and the efficiency of the proposed structure in improving the classification accuracy.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model’s architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model’s learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model’s ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.

Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.

Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision-making. Skin lesion segmentation from images is a crucial step toward achieving this goal-timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non-malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian-Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.

Skin lesion classification using transfer learning

Skin lesion classification using transfer learning

LW-XNet for segmentation and classification of skin lesions from dermoscopy images

The skin, one of the most crucial organs of the human body, serves as a barrier between the body and the external environment. Early detection of skin diseases is imperative to reduce mortality rates, as some untreated conditions may progress to skin cancer. Segmentation and classification of lesions represent pivotal and interrelated endeavors within the realm of skin disease diagnosis. For this purpose, this paper presents a comprehensive diagnostic framework for segmentation and classification of skin lesions, which integrates a Legendre multiwavelet transform-based fusion XNet (LW-XNet) with an improved soft attention dense connection convolutional network (ISA-DenseNet). LW-XNet combines the strengths of XNet in fusing different frequency components of images, along with the strong feature representation capability of LW bases with various regularities for overall contextual information and detailed information of dermoscopy images. Furthermore, its encoder devises a LWT channel concatenate (LCC) block to subdivide the image into eight wavelet coefficient feature images and perform concatenated processing on them, enabling it to better differentiate and comprehend the intricate features within dermoscopy images. Finally, ISA-DenseNet is utilized for multi-class classification of the segmented images. Experimental results demonstrate the superiority of the proposed framework over existing segmentation and classification methods.

A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability.

Deep Learning Algorithms for the Detection of Suspicious Pigmented Skin Lesions in Primary Care Settings: A Systematic Review and Meta-Analysis.

Early detection of suspicious pigmented skin lesions is crucial for improving the outcomes and survival rates of skin cancers. However, the accuracy of clinical diagnosis by primary care physicians (PCPs) is suboptimal, leading to unnecessary referrals and biopsies. In recent years, deep learning (DL)algorithms have shown promising results in the automated detection and classification of skin lesions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of DL algorithms for the detection of suspicious pigmented skin lesions in primary care settings. A comprehensive literature search was conducted using electronic databases, including PubMed, Scopus, IEEE Xplore, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science. Data from eligible studies were extracted, including study characteristics, sample size, algorithm type, sensitivity, specificity, diagnostic odds ratio (DOR), positivelikelihood ratio (PLR), negative likelihood ratio(NLR), and receiver operating characteristic curve analysis. Three studies were included. The results showed that DL algorithms had a high sensitivity (90%, 95% CI: 90-91%) and specificity (85%, 95% CI: 84-86%) for detecting suspicious pigmented skin lesions in primary care settings. Significant heterogeneity was observed in both sensitivity (p = 0.0062, I2 = 80.3%) and specificity (p < 0.001, I2 = 98.8%). The analysis of DOR and PLR further demonstrated the strong diagnostic performance of DL algorithms. The DOR was 26.39, indicating a strong overall diagnostic performance of DL algorithms. The PLR was 4.30, highlighting the ability of these algorithms to influence diagnostic outcomes positively. The NLR was 0.16, indicating that a negative test result decreased the odds of misdiagnosis. The area under the curve of DL algorithms was 0.95, indicating excellent discriminative ability in distinguishing between benign and malignant pigmented skin lesions. DL algorithms have the potential to significantly improve the detection of suspicious pigmented skin lesions in primary care settings. Our analysis showed that DL exhibited promising performance in the early detection of suspicious pigmented skin lesions. However, further studies are needed.

Skin Lesion Prediction and Classification Using Innovative Modified Long Short-Term Memory-Based Hybrid Optimization Algorithm

Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.

Skin Lesion Classification via ensemble method on deep learning

Skin Lesion Classification via ensemble method on deep learning

Skin lesion classification using modified deep and multi-directional invariant handcrafted features

Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer.

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.

Skin lesion classification with multi-level fusion of Swin-T and ConvNeXt

Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.

Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification.

Skin lesion classification is vital for the early detection and diagnosis of skin diseases, facilitating timely intervention and treatment. However, existing classification methods face challenges in managing complex information and long-range dependencies in dermoscopic images. Therefore, this research aims to enhance the feature representation by incorporating local, global, and hierarchical features to improve the performance of skin lesion classification. We introduce a novel dual-track deep learning (DL) model in this research for skin lesion classification. The first track utilizes a modified Densenet-169 architecture that incorporates a Coordinate Attention Module (CoAM). The second track employs a customized convolutional neural network (CNN) comprising a Feature Pyramid Network (FPN) and Global Context Network (GCN) to capture multiscale features and global contextual information. The local features from the first track and the global features from second track are used for precise localization and modeling of the long-range dependencies. By leveraging these architectural advancements within the DenseNet framework, the proposed neural network achieved better performance compared to previous approaches. The network was trained and validated using the HAM10000 dataset, achieving a classification accuracy of 93.2%.