Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models.

Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.

HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention.

Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively.

DBNet-SI: Dual branch network of shift window attention and inception structure for skin lesion segmentation.

The U-shaped convolutional neural network (CNN) has attained remarkable achievements in the segmentation of skin lesion. However, given the inherent locality of convolution, this architecture cannot capture long-range pixel dependencies and multiscale global contextual information effectively. Moreover, repeated convolutions and downsampling operations can readily result in the omission of intricate local fine-grained details. In this paper, we proposed a U-shaped network (DBNet-SI) equipped with a dual-branch module that combines shift window attention and inception structures. First, we proposed a dual-branch module that combines shift window attention and inception structures (MSI) to better capture multiscale global contextual information and long-range pixel dependencies. Specifically, we have devised a cross-branch bidirectional interaction module within the MSI module to enable information complementarity between the two branches in the channel and spatial dimensions. Therefore, MSI is capable of extracting distinguishing and comprehensive features to accurately identify the skin lesion boundaries. Second, we have devised a progressive feature enhancement and information compensation module (PFEIC), which progressively compensates for fine-grained features through reconstructed skip connections and integrated global context attention modules. The results of the experiment show the superior segmentation performance of DBNet-SI compared with other deep learning models for skin lesion segmentation in the ISIC2017 and ISIC2018 datasets. Ablation studies demonstrate that our model can effectively extract rich multiscale global contextual information and compensate for the loss of local details.

Understanding skin color bias in deep learning-based skin lesion segmentation.

BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic image classification against darker skin tones due to dataset imbalance and contrast disparities are acknowledged, a comprehensive exploration of skin color bias in lesion segmentation models is lacking. It is imperative to address and understand the biases in these models. METHODS: Our study comprehensively evaluates skin tone bias within prevalent neural networks for skin lesion segmentation. Since no information about skin color exists in widely used datasets, to quantify the bias we use three distinct skin color estimation methods: Fitzpatrick skin type estimation, Individual Typology Angle estimation as well as manual grouping of images by skin color. We assess bias across common models by training a variety of U-Net-based models on three widely-used datasets with 1758 different dermoscopic and clinical images. We also evaluate commonly suggested methods to mitigate bias. RESULTS: Our findings expose a significant and large correlation between segmentation performance and skin color, revealing consistent challenges in segmenting lesions for darker skin tones across diverse datasets. Using various methods of skin color quantification, we have found significant bias in skin lesion segmentation against darker-skinned individuals when evaluated both in and out-of-sample. We also find that commonly used methods for bias mitigation do not result in any significant reduction in bias. CONCLUSIONS: Our findings suggest a pervasive bias in most published lesion segmentation methods, given our use of commonly employed neural network architectures and publicly available datasets. In light of our findings, we propose recommendations for unbiased dataset collection, labeling, and model development. This presents the first comprehensive evaluation of fairness in skin lesion segmentation.

SG-MIAN: Self-guided multiple information aggregation network for image-level weakly supervised skin lesion segmentation.

Nowadays, skin disease is becoming one of the most malignant diseases that threaten people's health. Computer aided diagnosis based on deep learning has become a widely used technology to assist medical professionals in diagnosis, and segmentation of lesion areas is one of the most important steps in it. However, traditional medical image segmentation methods rely on numerous pixel-level labels for fully supervised training, and such labeling process is time-consuming and requires professional competence. In order to reduce the costs of pixel-level labeling, we proposed a method only using image-level label to segment skin lesion areas. Due to the lack of lesion's spatial and intensity information in image-level labels, and the wide distribution range of irregular shape and different texture on skin lesions, the algorithm must pay great attention to the automatic lesion localization and perception of lesion boundary. In this paper, we proposed a Self-Guided Multiple Information Aggregation Network (SG-MIAN). Our backbone network MIAN utilizes the Multiple Spatial Perceptron (MSP) solely using classification information as guidance to discriminate the key classification features of lesion areas, and thereby performing more accurate localization and activation of lesion areas. Additionally, adjunct to MSP, we also proposed an Auxiliary Activation Structure (AAS) and two auxiliary loss functions to further self-guided boundary correction, achieving the goal of accurate boundary activation. To verify the effectiveness of the proposed method, we conducted extensive experiments using the HAM10000 dataset and the PH2dataset, which demonstrated superior performance compared to most existing weakly supervised segmentation methods.

Utility of Dermoscopy Training in Improving Diagnostic Accuracy of Skin Lesions Among Physician Assistant Students.

INTRODUCTION: Patients often first present to their primary care provider for skin lesion concerns, and dermoscopy is a tool that enhances diagnostic acumen of both malignant and benign skin lesions. Physician assistants (PAs) frequently serve as primary care and dermatology providers, but to our knowledge, no current research on dermoscopy expertise with PAs exists. We hypothesize that PA students could be taught dermoscopy based on the triage amalgamated dermoscopic algorithm (TADA) to increase their diagnostic skill, as previously shown with medical students. METHODS: Dermoscopy was taught to first-year PA students at all 5 PA programs in the state of Minnesota. The training was 50 minutes in length and focused on the fundamentals of the TADA method. Physician assistant students participated in a pretraining and post-training test, consisting of 30 dermoscopic images. RESULTS: A total of 139/151 (92%) PA students completed both the pretraining and post-training tests. Overall, mean scores for all students increased significantly ( P < .0001) after dermoscopy training was given (18.5 ± 7.1 vs. 23.8 ± 6.7). CONCLUSION: Our study demonstrates that after TADA training, PA students improved their ability to assess dermoscopy images of both skin cancer and benign lesions accurately, suggesting that PAs can be trained as novice dermoscopists and provide better dermatologic care to patients. We strongly encourage integration of dermoscopy into didactic education across PA programs. Implementing a dermoscopy curriculum in established PA programs will enable future PAs to provide better clinical care when evaluating skin lesions.

HSH-UNet: Hybrid selective high order interactive U-shaped model for automated skin lesion segmentation.

The use of computer-assisted clinical dermatologists to diagnose skin diseases is an important aid. And computer-assisted techniques mainly use deep neural networks. Recently, the proposal of higher-order spatial interaction operations in deep neural networks has attracted a lot of attention. It has the advantages of both convolution and transformers, and additionally has the advantages of efficient, extensible and translation-equivariant. However, the selection of the interaction order in higher-order interaction operations requires tedious manual selection of a suitable interaction order. In this paper, a hybrid selective higher-order interaction U-shaped model HSH-UNet is proposed to solve the problem that requires manual selection of the order. Specifically, we design a hybrid selective high-order interaction module HSHB embedded in the U-shaped model. The HSHB adaptively selects the appropriate order for the interaction operation channel-by-channel under the computationally obtained guiding features. The hybrid order interaction also solves the problem of fixed order of interaction at each level. We performed extensive experiments on three public skin lesion datasets and our own dataset to validate the effectiveness of our proposed method. The ablation experiments demonstrate the effectiveness of our hybrid selective higher order interaction module. The comparison with state-of-the-art methods also demonstrates the superiority of our proposed HSH-UNet performance. The code is available at https://github.com/wurenkai/HSH-UNet.

MSS-UNet: A Multi-Spatial-Shift MLP-based UNet for skin lesion segmentation.

Multilayer perceptron (MLP) networks have become a popular alternative to convolutional neural networks and transformers because of fewer parameters. However, existing MLP-based models improve performance by increasing model depth, which adds computational complexity when processing local features of images. To meet this challenge, we propose MSS-UNet, a lightweight convolutional neural network (CNN) and MLP model for the automated segmentation of skin lesions from dermoscopic images. Specifically, MSS-UNet first uses the convolutional module to extract local information, which is essential for precisely segmenting the skin lesion. We propose an efficient double-spatial-shift MLP module, named DSS-MLP, which enhances the vanilla MLP by enabling communication between different spatial locations through double spatial shifts. We also propose a module named MSSEA with multiple spatial shifts of different strides and lighter external attention to enlarge the local receptive field and capture the boundary continuity of skin lesions. We extensively evaluated the MSS-UNet on ISIC 2017, 2018, and PH2 skin lesion datasets. On three datasets, the method achieves IoU metrics of 85.01%±0.65, 83.65%±1.05, and 92.71%±1.03, with a parameter size and computational complexity of 0.33M and 15.98G, respectively, outperforming most state-of-the-art methods.The code is publicly available at https://github.com/AirZWH/MSS-UNet.

Deeply Supervised Skin Lesions Diagnosis With Stage and Branch Attention.

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin diseases. Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment. Recent studies have reported that ensembled convolutional neural networks (CNNs) are practical to classify the images for early diagnosis of skin disorders. However, the practical use of these ensembled CNNs is limited as these networks are heavyweight and inadequate for processing contextual information. Although lightweight networks (e.g., MobileNetV3 and EfficientNet) were developed to achieve parameter reduction for implementing deep neural networks on mobile devices, insufficient depth of feature representation restricts the performance. To address the existing limitations, we develop a new lite and effective neural network, namely HierAttn. The HierAttn applies a novel deep supervision strategy to learn the local and global features by using multi-stage and multi-branch attention mechanisms with only one training loss. The efficacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD2020). The experimental results show that HierAttn achieves the best accuracy and area under the curve (AUC) among the state-of-the-art lightweight networks.

Evaluating medical undergraduates' and dermatology faculty members' perception of using three-dimensional images as part of dermatology education in a time of COVID-19: a mixed-methods study.

BACKGROUND: The coronavirus-19 pandemic has impacted the delivery of medical education in dermatology, leading to decreased patient contact. There arose a need to pioneer innovative teaching tools to augment current methods for now and beyond the pandemic. OBJECTIVES: We aimed to assess the utility of three-dimensional (3D) images in the learning and teaching of dermatology by analysing the perceptions of medical undergraduates and faculty members in a qualitative and quantitative study. METHODS: Medical undergraduates (n = 119) and dermatology faculty members (n = 20) were recruited on a voluntary basis to watch a showcase session using a portable 3D imaging system allowing 3D images of skin lesions to be examined and digitally manipulated. After the session, participants filled in an anonymous questionnaire evaluating their perceptions. RESULTS: Of the 119 learners, most (> 84%) strongly agreed/agreed that (i) they would have more confidence in the field of dermatology; (ii) their ability to describe skin lesions would increase; (iii) their understanding of common dermatological conditions would increase; (iv) 3D images allow a greater approximation to real-life encounters than 2D images; and (v) learning with this modality would be useful. Of the 20 faculty members, most (> 84%) strongly agreed/agreed that (i) it is easier to teach with the aid of 3D images, and (ii) they would want access to 3D images during teaching sessions. Skin tumours were perceived to be learnt best via this modality in terms of showcasing topography (P < 0.01) and close approximation to real-life (P < 0.001). Overall, thematic analysis from qualitative analysis revealed that conditions learnt better with 3D images were those with surface changes and characteristic topography. CONCLUSIONS: Our results show that the greatest utility of 3D images lies in conditions where lesions have skin surface changes in the form of protrusions or depressions, such as in skin tumours or ulcers. As such, 3D images can be useful teaching tools in dermatology, especially in conditions where appreciation of surface changes and topography is important.

CR-Conformer: a fusion network for clinical skin lesion classification.

Deep convolutional neural network (DCNN) models have been widely used to diagnose skin lesions, and some of them have achieved diagnostic results comparable to or even better than dermatologists. Most publicly available skin lesion datasets used to train DCNN were dermoscopic images. Expensive dermoscopic equipment is rarely available in rural clinics or small hospitals in remote areas. Therefore, it is of great significance to rely on clinical images for computer-aided diagnosis of skin lesions. This paper proposes an improved dual-branch fusion network called CR-Conformer. It integrates a DCNN branch that can effectively extract local features and a Transformer branch that can extract global features to capture more valuable features in clinical skin lesion images. In addition, we improved the DCNN branch to extract enhanced features in four directions through the convolutional rotation operation, further improving the classification performance of clinical skin lesion images. To verify the effectiveness of our proposed method, we conducted comprehensive tests on a private dataset named XJUSL, which contains ten types of clinical skin lesions. The test results indicate that our proposed method reduced the number of parameters by 11.17 M and improved the accuracy of clinical skin lesion image classification by 1.08%. It has the potential to realize automatic diagnosis of skin lesions in mobile devices.

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

INTRODUCTION: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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.

Impact of artificial intelligence-based color constancy on dermoscopical assessment of skin lesions: A comparative study.

BACKGROUND: The quality of dermoscopic images is affected by lighting conditions, operator experience, and device calibration. Color constancy algorithms reduce this variability by making images appear as if they were acquired under the same conditions, allowing artificial intelligence (AI)-based methods to achieve better results. The impact of color constancy algorithms has not yet been evaluated from a clinical dermatologist's workflow point of view. Here we propose an in-depth investigation of the impact of an AI-based color constancy algorithm, called DermoCC-GAN, on the skin lesion diagnostic routine. METHODS: Three dermatologists, with different experience levels, carried out two assignments. The clinical experts evaluated key parameters such as perceived image quality, lesion diagnosis, and diagnosis confidence. RESULTS: When the DermoCC-GAN color constancy algorithm was applied, the dermoscopic images were perceived to be of better quality overall. An increase in classification performance was observed, reaching a maximum accuracy of 74.67% for a six-class classification task. Finally, the use of normalized images results in an increase in the level of self-confidence in the qualitative diagnostic routine. CONCLUSIONS: From the conducted analysis, it is evident that the impact of AI-based color constancy algorithms, such as DermoCC-GAN, is positive and brings qualitative benefits to the clinical practitioner.

A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population.

In recent years, numerous dermatological image databases have been published to make possible the development and validation of artificial intelligence-based technologies to support healthcare professionals in the diagnosis of skin diseases. However, the generation of these datasets confined to certain countries as well as the lack of demographic information accompanying the images, prevents having a real knowledge of in which populations these models could be used. Consequently, this hinders the translation of the models to the clinical setting. This has led the scientific community to encourage the detailed and transparent reporting of the databases used for artificial intelligence developments, as well as to promote the formation of genuinely international databases that can be representative of the world population. Through this work, we seek to provide details of the processing stages of the first public database of dermoscopy and clinical images created in a hospital in Argentina. The dataset comprises 1,616 images corresponding to 1,246 unique lesions collected from 623 patients.

Generative adversarial networks based skin lesion segmentation.

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path, an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. A morphology-based smoothing loss is also implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration Lesion Dataset. It outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and accuracy of 90.1%, 83.6%, and 94.5%, respectively. We also design a lightweight segmentation framework called Mobile-GAN (MGAN) that achieves comparable performance as EGAN but with an order of magnitude lower number of training parameters, thus resulting in faster inference times for low compute resource settings.

Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis: Complementary information that may affect decision-making.

In dermatology, deep learning may be applied for skin lesion classification. However, for a given input image, a neural network only outputs a label, obtained using the class probabilities, which do not model uncertainty. Our group developed a novel method to quantify uncertainty in stochastic neural networks. In this study, we aimed to train such network for skin lesion classification and evaluate its diagnostic performance and uncertainty, and compare the results to the assessments by a group of dermatologists. By passing duplicates of an image through such a stochastic neural network, we obtained distributions per class, rather than a single probability value. We interpreted the overlap between these distributions as the output uncertainty, where a high overlap indicated a high uncertainty, and vice versa. We had 29 dermatologists diagnose a series of skin lesions and rate their confidence. We compared these results to those of the network. The network achieved a sensitivity and specificity of 50% and 88%, comparable to the average dermatologist (respectively 68% and 73%). Higher confidence/less uncertainty was associated with better diagnostic performance both in the neural network and in dermatologists. We found no correlation between the uncertainty of the neural network and the confidence of dermatologists (R = -0.06, p = 0.77). Dermatologists should not blindly trust the output of a neural network, especially when its uncertainty is high. The addition of an uncertainty score may stimulate the human-computer interaction.

Reconstruction of dermal layers evaluated by high-frequency ultrasound following treatment for skin fibrosis.

Background: Lymphedema is a chronic, progressive clinical condition that evolves with intense fibrosis, the most advanced stage of which is stage III (lymphostatic fibrosclerosis). Aim: The aim of the present study was to show the possibility to reconstruct the dermal layers with the intensive treatment of fibrosis using the Godoy method. Case description: A 55-year-old patient with an eight-year history of edema of the lower limb of the leg had constant episodes of erysipelas, despite regular treatments. The edema progressed continually, associated with a change in the color of the skin and the formation of a crust. Intensive treatment (eight hours per day for three weeks) was proposed with the Godoy method. The ultrasound was performed and results revealed substantial improvement in the skin, with the onset of the reconstruction of the dermal layers. Conclusion: It is possible to reconstruct the layers of the skin in fibrotic conditions caused by lymphedema.

Deep Learning-Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement.

Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have some drawbacks, and the prediction is not highly accurate. This is where a dependable automatic recognition system for skin cancers comes into play. With the extensive usage of deep learning in various aspects of medical health, a novel computer-aided dermatologist tool has been suggested for the accurate identification and classification of skin lesions by deploying a novel deep convolutional neural network (DCNN) model that incorporates global average pooling along with preprocessing to discern the skin lesions. The proposed model is trained and tested on the HAM10000 dataset, which contains seven different classes of skin lesions as target classes. The black hat filtering technique has been applied to remove artifacts in the preprocessing stage along with the resampling techniques to balance the data. The performance of the proposed model is evaluated by comparing it with some of the transfer learning models such as ResNet50, VGG-16, MobileNetV2, and DenseNet121. The proposed model provides an accuracy of 97.20%, which is the highest among the previous state-of-art models for multi-class skin lesion classification. The efficacy of the proposed model is also validated by visualizing the results obtained using a graphical user interface (GUI).

Skin lesion image segmentation based on lightweight multi-scale U-shaped network.

UNet, and more recently medical image segmentation methods, utilize many parameters and computational quantities to achieve higher performance. However, due to the increasing demand for real-time medical image segmentation tasks, it is important to trade between accuracy rates and computational complexity. To this end, we propose a lightweight multi-scale U-shaped network (LMUNet), a multi-scale inverted residual and an asymmetric atrous spatial pyramid pooling-based network for skin lesion image segmentation. We test LMUNet on multiple medical image segmentation datasets, which show that it reduces the number of parameters by 67X and decreases the computational complexity by 48X while obtaining better performance over the partial lightweight networks.