Skin Lesion Classification Research Articles

A Comprehensive Review on Monkeypox Skin Lesion Recognition through Deep Learning

This comprehensive review delves into the emerging field of Monkeypox skin lesion recognition using deep learning techniques. Monkeypox, a rare viral disease with symptoms resembling smallpox, presents a diagnostic challenge, particularly in resource-limited regions. The paper explores the recent advancements in deep learning methodologies applied to the automated identification and classification of Monkeypox skin lesions, offering a detailed analysis of various neural network architectures, image preprocessing techniques, and dataset considerations. The review highlights the potential of deep learning models in enhancing the accuracy and efficiency of Monkeypox diagnosis, paving the way for improved early detection and timely intervention in affected populations. Additionally, it discusses challenges and future directions in this domain, emphasizing the need for robust and interpretable models to facilitate widespread adoption in clinical settings.

PCA: Progressive class-wise attention for skin lesions diagnosis

Skin cancer is the most prevalent type of cancer worldwide. Early detection is essential as it could be fatal at later stages. The classification of skin lesions is challenging since there are many variations, including changes in color, shape, size, high intra-class variation, and high inter-class similarity. In this paper, a unique class-wise attention method is proposed that considers each class equally while extracting additional discriminative information of skin lesions. The proposed attention mechanism is employed in a progressive manner to incorporate discriminative feature information from multiple scales. The proposed approach obtained competitive performance against more than 15 state-of-the-art methods including HAM1000 and ISIC 2019 leaderboard winners. The proposed method achieved 97.40% accuracy on the HAM10000 and 94.9% accuracy on the ISIC 2019 dataset.

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things.

Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

Boosting the performance of pretrained CNN architecture on dermoscopic pigmented skin lesion classification.

Pigmented skin lesions (PSLs) pose medical and esthetic challenges for those affected. PSLs can cause skin cancers, particularly melanoma, which can be life-threatening. Detecting and treating melanoma early can reduce mortality rates. Dermoscopic imaging offers a noninvasive and cost-effective technique for examining PSLs. However, the lack of standardized colors, image capture settings, and artifacts makes accurate analysis challenging. Computer-aided diagnosis (CAD) using deep learning models, such as convolutional neural networks (CNNs), has shown promise by automatically extracting features from medical images. Nevertheless, enhancing the CNN models' performance remains challenging, notably concerning sensitivity. In this study, we aim to enhance the classification performance of selected pretrained CNNs. We use the 2019 ISIC dataset, which presents eight disease classes. To achieve this goal, two methods are applied: resolution of the dataset imbalance challenge through augmentation and optimization of the training hyperparameters via Bayesian tuning. The performance improvement was observed for all tested pretrained CNNs. The Inception-V3 model achieved the best performance compared to similar results, with an accuracy of 96.40% and an AUC of 0.98. According to the study, classification performance was significantly enhanced by augmentation and Bayesian hyperparameter tuning.

Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

This manuscript proposes the possibility of concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eight lesions were analyzed using some algorithms of artificial intelligence: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASCs). Eleven artificial intelligence models were applied so that eight skin lesions could be classified by analyzing the signatures of each lesion. The database was randomly divided into 80% and 20% for the training and test dataset images, respectively. The metrics that are reported are accuracy, sensitivity, specificity, and precision. Each process was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the averages and ± standard deviations were reported for each metric. Although all the results were very satisfactory, the highest average score for the eight lesions analyzed was obtained using the subspace k-NN model, where the test metrics were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.

Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN)

The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance.

DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images.

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models' performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

Artificial Intelligence based real-time automatic detection and classification of skin lesion in dermoscopic samples using DenseNet-169 architecture

The most common challenge faced by dermoscopy images is the automatic detection of lesion features. All the existing solutions focus on complex algorithms to provide accurate detections. In this research work, proposed Online Tigerclaw Fuzzy Region Segmentation with Deep Learning Classification model, an intellectual model is proposed that provides discrimination of features with classification even in fine-grained samples. This model works on four different stages, which include the Boosted Anisotropic Diffusion filter with Recursive Pixel Histogram Equalization (BADF-RPHE) in the preprocessing stage. The next step is the proposed Online Tigerclaw Fuzzy Region Segmentation (OTFRS) algorithm for lesion area segmentation of dermoscopic images, which can achieve 98.9% and 97.4% accuracy for benign and malignant lesions, respectively. In the proposed OTFRS, an accuracy improvement of 1.4% is achieved when compared with previous methods. Finally, the increased robustness of lesion classification is achieved using Deep Learning Classification –DenseNet 169 with 500 images. The proposed approach was evaluated with accuracy classifications of 100% and 98.86% for benign and malignant lesions, respectively, and a processing time of less than 18 sec. In the proposed DensetNet-169 classification technique, an accuracy improvement of 3% is achieved when compared with other state-of-art methods. A higher range of true positive values is obtained for the Region of Convergence (ROC) curve, which indicates that the proposed work ensures better performance in clinical diagnosis for accurate feature visualization analysis. The methodology has been validated to prove its effectiveness and throw light on the lives of affected patients so they can resume normalcy and live long. The research work was tested in real-time clinical samples, which delivered promising and encouraging results in skin cell detection procedures.

Skin lesion classification using HG-PSO and YOLOv7 based convolutional network in real time.

Skin cancer is a chronic illness seen visually and further diagnosed with a dermoscopic examination. It is crucial to precisely localize and classify lesions from dermoscopic images to diagnose and treat skin cancers as soon as possible. This work presents melanoma identification, and the classification method significantly improves accuracy and precision. This work proposes a method Hybrid of Genetic and Particle swarm optimization (HG-PSO), and You only look once version 7 (YOLOv7) based convolutional network for skin cancer classification. The infected region is first located using optimized YOLOv7 object detection. Then color thresholding is applied to segment it, which is passed to the proposed convolutional network for classification. This work is tested on the Human Against Machine with 10,000 training images (HAM10000), International Skin Imaging Collaboration (ISIC)-2019, and Hospital Pedro Hispano (PH2) datasets, and the findings are compared to the state-of-the-art methods for classifying skin cancer. The proposed method achieves 98.86% accuracy, 99.00% average precision, 98.85% average recall, and 98.85% average F1-score on the HAM10000 dataset. It achieves 97.10% accuracy on ISIC-2019 datasets. The average precision obtained is 97.37%, the average recall is 97.13%, and the average F1-score is 97.13% on the ISIC-2019 dataset. It achieves a 97.7% accuracy on the PH2 dataset. The average precision obtained is 99.00%, the average recall is 96.00%, and the average F1-score is 97.00% on the PH2 dataset. The test time taken by this method on datasets HAM10000, ISIC-2019, and PH2 dataset is 2, 3, and 2 s, respectively, which may help give faster responses in telemedicine.

Melanoma Skin Cancer Detection and Classification using Deep Learning

Abstract: Melanoma is caused by abnormal growth of skin cells which is most often devloped on skin exposed to UV rays. It is less common but one of the most dangerous diseases in the world. Classifying skin lesions correctly at an early stage could avoid some types of skin cancers like melanoma and increases the chances of cure before cancer spreads. One of the efficient methods to accurately identify Melanoma is using deep learning. Deep learning methods such as AlexNet, Inception V3 were proposed in the past. In this project, other methods like MobileNet, CNN, ensemble method, ReNet, VGG16 will be used to detect the type of cancer using dermoscopic image data. Layers of these models are applied for fine-tuning which allows us to differentiate between different classes of skin lesions. There are two kinds of Melanoma, malignant and benign and we will also perform grading to early diagnose the disease which will ultimately increase the survival rate.

Monkeypox Detection Using Hyper-Parameter Tuned Based Transferable CNN Model

The reemergence of Monkeypox, a communicable illness resulting from the Monkeypox virus, has raised apprehensions about a potential swift global pandemic similar to the COVID-19 epidemic as COVID-19 infections diminish globally. The prompt emphasizes the criticality of prompt action within communities to mitigate the development of the phenomenon. The timely identification and accurate categorization of Monkeypox cutaneous manifestations are crucial for the successful implementation of containment strategies. This paper presents a novel methodology for detecting Monkeypox by utilizing a transferrable Convolutional Neural Network (CNN) model that has been optimized utilizing hyper-parameter tuning techniques. The proposed methodology initiates by improving the quality of the original Monkeypox images, with a specific emphasis on boosting edge details to increase visual clarity. Texture qualities are obtained through an energy layer, enhancing distinctive traits. Our methodology's cornerstone is utilizing the Hyper-parameter-based transferable Convolutional Neural Network (HPT-TCNN), specifically designed to enhance classification accuracy.
 In contrast to traditional methods, we enhance the architectural design by replacing the pooling layer with a configuration comprising three convolutional layers and one energy layer. The hyper-parameter tuning procedure is optimized by employing the Optimisation Algorithm known as MGS-ROA. In order to enhance the process of model training and validation, we have assembled the "Monkeypox Skin Lesion Dataset (MSLD)," which consists of a collection of images depicting human skin lesions produced by Monkeypox. The dataset in question is vital in evaluating and improving our methodology. In a comparison analysis conducted on other deep learning models, the suggested model has superior performance compared to other models, obtaining a notable accuracy, sensitivity, and specificity, all reaching a value of 93.60%. The outstanding performance shown in this study highlights the methodology's effectiveness in adequately classifying skin lesions associated with Monkeypox. This approach shows potential for physicians and healthcare workers since it facilitates early detection, a crucial factor in preventing the spread of Monkeypox.

Özel Derin Konvolüsyonel Sinir Ağı Kullanarak Cilt Kanseri Tanıma

Skin cancer is a common form of cancer that affects millions of people worldwide. Early detection and accurate diagnosis of skin cancer are crucial for effective treatment and management of the disease. There has been a growing interest in using deep learning techniques and computer vision algorithms to develop automated skin cancer detection systems in recent years. Among these techniques, convolutional neural networks (CNN) have shown remarkable performance in detecting and classifying skin lesions. This paper presents a comprehensive study using CNN and deep learning techniques for skin cancer detection using the International Skin Imaging Collaboration (ISIC) dataset. The proposed architecture is a compact deep CNN that is trained using a dataset of benign and malignant skin lesion images. The proposed architecture has achieved 84.8% accuracy, 83.8% TPR, 83.7% TNR, 81.6% F1-score and 80.5% precision for performance evaluation. The experimental results show promising results for the accurate and efficient detection of skin cancer, which has the potential to improve the diagnosis and treatment of this life-threatening disease.

Classification of skin lesion using deep convolutional neural network by applying transfer learning

<p>The early and accurate diagnosis of skin cancer is crucial for improving patient outcomes and reducing the need for invasive biopsies. This study proposes a deep learning model for classifying skin malignancy using transfer learning and data augmentation techniques to address limitations observed in previous models and enhance diagnostic accuracy. The approach involves applying transfer learning to a pre-trained ResNet152 architecture using tensorflow and keras. Data augmentation techniques are employed on a dataset consisting of 10,015 skin lesion images obtained from the international skin imaging collaboration (ISIC) 2018 challenge, which encompasses diverse lesion types, sizes, and colors, posing a challenging classification task. Binary cross-entropy serves as the loss function, and the Adam optimizer is utilized for training the model. The results demonstrate a specificity of 87.42% and an F1 score of 0.854, outperforming other models in the field. These statistical findings highlight the effectiveness of transfer learning and data augmentation techniques in improving the accuracy of skin cancer diagnosis. The novelty of this study lies in the combination of transfer learning and data augmentation methods to enhance diagnostic accuracy. However, it is important to acknowledge the limitations of this study, including the necessity for further investigation to evaluate the clinical practicality of the model and address potential biases. Future research could explore the application of this model in a clinical setting and the development of models for detecting other types of skin lesions. In conclusion, the proposed deep learning model based on the ResNet152 architecture showcases promising results in the classification of skin lesions, demonstrating its potential for accurate skin cancer diagnosis. With further research and improvement, these models have the potential to revolutionize healthcare, improving patient outcomes, reducing healthcare costs, and increasing accessibility to screening and diagnosis, particularly for underserved populations.</p>

NEURAL NETWORK SYSTEM FOR ANALYSIS OF UNBALANCED HETEROGENEOUS DERMATOLOGICAL DATA ON THE BASIS OF A MULTIMODAL NEURAL NETWORK

To date, skin cancer is the most frequently diagnosed form of oncopathology. Visual diagnosis of this cancer type in the early stages is difficult due to similar visual manifestations in benign and malignant types of pigmented formations, and its accuracy depends on the experience of dermatologist. Artificial intelligence technologies are able to equal and surpass accuracy of visual diagnostic methods, but there is a risk of false positive prognosis, when malignant pigmented formation may be recognized as benign. One of the possible ways to improve recognition accuracy and reduce false positive conclusions is simultaneous use of different preprocessing methods, heterogeneous data analysis, and use of modified learning loss functions to eliminate negative impact of unbalanced dermatological data. The study presents an intelligent system for unbalanced heterogeneous dermatological data analyzing based on a multimodal neural network trained using modified cross-entropy loss function. The accuracy of classification of pigmented skin lesions in 10 diagnostically significant categories in the proposed system based on the AlexNet convolutional neural network architecture reached 83.87%. Due to the emerging synergy when using various methods to improve performance quality of intelligent systems, the number of false positive forecasts of the intelligent system was reduced, and the accuracy was increased by 1.31-4.91 percentage points, depending on the convolutional neural network architecture. Implementation of the developed system as a tool for auxiliary diagnostics can reduce the consumption of financial and labor resources involved in the medical industry, as well as increase the chance of early detection of pigmentary oncopathologies.

Ti-FCNet: Triple fused convolutional neural network-based automated skin lesion classification

Ti-FCNet: Triple fused convolutional neural network-based automated skin lesion classification

Deep Learning Framework for Identification of Skin Lesions

Skin ailments don't just affect the physical appearance of an individual but also lead to psychological issues. Vitiligo and discoloration patches are such conditions that can negatively impact one's self-assurance. Here, authors have designed 14 distinct models to classify skin lesions using the HAM10000 dataset which is sorted into 7 classes including Actinic Keratosis, Melanocytic nevi, Actinic keratoses, Melanoma, Benign keratosis-like lesions, Basal cell carcinoma, and Vascular lesions. Further, authors compared their model against other state-of-the-art models, and additional-ly employed various pre-trained models like Resnet50, InceptionV3, MobileNetV2, Densenet201, VGG16, VGG19, InceptionResnetv2, Xception, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, Effi-cientNetB4, EfficientNetB5 that were trained on image net datasets. Their primary aim was to develop a framework that can be implemented in real-world applications using Efficient Nets. Experimental evaluations have shown that their proposed models have outperformed traditional pre-trained models like ResNets and VGG16 in terms of accuracy, precision, re-call, and validation loss, despite being lightweight. Interestingly, this im-provement was achieved without any data augmentation techniques. The authors achieved accuracy above 90% for all the EfficientNet models (B0-B5), which was far better than the existing pre-trained models, thus establishing the supremacy of proposed model.

SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm.

Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top-bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.

Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients' long-term survival. Several computer-based methods have recently been proposed, in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. The primary objective of this study was to maximize the input feature information by combining multiple deep models in the first phase, and then to avoid noisy and redundant information by downsampling the feature set, using a novel evolutionary feature selection technique, in the second phase. By maintaining the integrity of the original feature space, the proposed idea generated highly discriminant feature information. Recent deep models, including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2, were employed in our study, for the purpose of feature extraction. Additionally, transfer learning was leveraged, to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models was combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection, using a novel entropy-controlled gray wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques was employed, initially to incorporate the feature vector with a high level of information and, subsequently, to eliminate redundant and irrelevant feature information. The effectiveness of our concept is supported by an assessment conducted on three benchmark dermoscopic datasets: PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation was conducted, including a rigorous comparison to established techniques in the field.

Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System

In this work, the federated learning mechanism is introduced into the deep learning of medical models in Internet of Things (IoT)-based healthcare system. Cryptographic primitives, including masks and homomorphic encryption, are applied for further protecting local models, so as to prevent the adversary from inferring private medical data by various attacks such as model reconstruction attack or model inversion attack, etc. The qualities of the datasets owned by different participants are considered as the main factor for measuring the contribution rate of the local model to the global model in each training epoch, instead of the size of datasets commonly used in deep learning. A dropout-tolerable scheme is proposed in which the process of federated learning would not be terminated if the number of online clients is not less than a preset threshold. Through the analysis of the security, it shows that the proposed scheme satisfies data privacy. Computation cost and communication cost are also analyzed theoretically. Finally, skin lesion classification using training images provided by the HAM10000 medical dataset is set as an example of healthcare applications. Experimental results show that compared with existing schemes, the proposed scheme obtained promising results while ensuring privacy preserving.