Recognition Of Skin Cancer Research Articles

Competency of Family Medicine Residents in Performing Skin Cancer Examination.

Early recognition of skin cancer reduces associated morbidity and improves survival. Most patients with suspicious skin lesions present to family medicine physicians. We aimed to evaluate the self-reported competency of family medicine residents in performing skin cancer examination (SCE) and assess the impact of different factors on their competency. All family medicine residents completed a previously validated questionnaire aimed at evaluating their skin cancer examination competency in this cross-sectional study. In total, 250 residents participated in this study. More than half of the residents never had a rotation in dermatology or received training under the supervision of a dermatologist. Approximately 70%, 72%, and 77% of residents had not observed SCE, received SCE training, and performed SCE, respectively. Although most residents were aware of the risk factors for skin cancer and the ABCD acronym for melanoma, only one out of ten residents screened their patients for these factors, enquired about changes in skin lesions, and personally practiced SCE. Only 16% of family medicine residents consider themselves competent in performing SCE. Training under the guidance of a dermatologist, SCE training, evaluating patients for cancer risk factors and any changes in moles, and SCE practice on personal level were associated with SCE competency. Multivariate analysis showed a significant association between the frequency of observing SCE and competency in performing SCE. Only 16% of family medicine residents considered themselves competent in performing SCE. The frequency of observation of SCE during residency was a significant predictor of self-reported competency in performing SCE.

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results.

54768 Skin Cancer Recognition and Efficacy of an Elastic-Scattering Spectroscopy device using Neural Networks (SCREENN)

54768 Skin Cancer Recognition and Efficacy of an Elastic-Scattering Spectroscopy device using Neural Networks (SCREENN)

Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures

Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures

Educational intervention targeting primary care residents improves skin cancer recognition in patients with skin of color

Educational intervention targeting primary care residents improves skin cancer recognition in patients with skin of color

Using an Improved Visual Geometry Group Neural Network for Skin Cancer Detection

Over time, deep learning has become more accurate and efficient at dealing with more complex risks. However, training deep neural networks remains a daunting problem and challenges remain, possibly due to several issues, including relevance, generalizability, and training time. Producing specific processors to manipulate artificial networks and reduce task-specific network learning time is natural, but the challenge of fit and generalizability remains undisputed. These networks have been used to detect and diagnose skin cancer, one of the most dangerous types of cancer caused by DNA damage. Automatic recognition of skin cancer is important to help doctors detect the disease in its early stages. In this research, the proposed vgg16 method was used to classify benign and malignant medical images. Image processing was used, where Gaussian noise and salt and pepper noise were added to increase the data, and filters for the arithmetic mean and median were added for each noise. The network was implemented using the Keras interface. Appling suggest model shown that the height accuracy 0.86 for train and accuracy 0.92 for test

Literature Review: Skin Cancer Types Detection Using Deep Learning

Skin cancer ranks among the swiftly proliferating diseases globally, exacerbated by the limited resources. Timely recognition of skin cancer holds paramount importance for precise diagnosis and identification, facilitating a preventative approach overall. The incidence of melanoma, the most perilous type of cancer of the skin, is increasing. Identifying skin cancer in its initial phases poses a challenge for dermatologists. Over the past few years, both supervised and unsupervised learning assignments have extensively employed deep learning techniques. Among these, Convolutional Neural Networks (CNN) has outperformed its counterparts in tests related to object detection and classification. The algorithm used here relies on a bank of directional filters (difference of Gaussians) and explores color, directionality and topological properties of the network. Dull Razor algorithm has been used to remove artifacts such as hair as they cause difficulties in detecting pigments. Keywords— Deep learning, Convolutional Neural Networks (CNN), Color analysis, Data Augmentation, Artifacts removal, Pigment detection.

Non-convex optimization with using positive-negative moment estimation and its application for skin cancer recognition with a neural network

The main problem of using standard optimization methods is the need to change all parameters in same-size steps, regardless of the behavior of the gradient. A more efficient way to optimize a neural network is to set adaptive step sizes for each parameter. Standard methods are based on the square roots of exponential estimates of the moments of the squares of past gradients and do not use the local variation in gradients. The paper presents methods of adaptive non-convex and belief-based optimization with a positive-negative estimate of the moments with the corresponding theoretical guarantees of convergence. These approaches allow the loss function to more accurately converge in the neighborhood of the global minimum in a smaller number of iterations. The utilization of transformed positive-negative moment estimates and an additional parameter that controls the step size allows one to avoid local extremes for achieving higher performance, compared to similar methods. The introduction of the developed algorithms into the learning process of various architectures of multimodal neural network systems for analyzing heterogeneous data has made it possible to increase the accuracy of recognizing pigmented skin lesions by 2.33 – 5.69 percentage points, compared to the original optimization methods. Multimodal neural network systems for analyzing heterogeneous dermatological data, using the proposed optimization algorithms, can be applied as a tool for auxiliary medical diagnostics, which will 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.

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

Skin cancer poses a serious risk to one’s health and can only be effectively treated with early detection. Early identification is critical since skin cancer has a higher fatality rate, and it expands gradually to different areas of the body. The rapid growth of automated diagnosis frameworks has led to the combination of diverse machine learning, deep learning, and computer vision algorithms for detecting clinical samples and atypical skin lesion specimens. Automated methods for recognizing skin cancer that use deep learning techniques are discussed in this article: convolutional neural networks, and, in general, artificial neural networks. The recognition of symmetries is a key point in dealing with the skin cancer image datasets; hence, in developing the appropriate architecture of neural networks, as it can improve the performance and release capacities of the network. The current study emphasizes the need for an automated method to identify skin lesions to reduce the amount of time and effort required for the diagnostic process, as well as the novel aspect of using algorithms based on deep learning for skin lesion detection. The analysis concludes with underlying research directions for the future, which will assist in better addressing the difficulties encountered in human skin cancer recognition. By highlighting the drawbacks and advantages of prior techniques, the authors hope to establish a standard for future analysis in the domain of human skin lesion diagnostics.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the “HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

Skin cancer recognition with novel deep learning methodology on mobile platform

Skin cancer recognition with novel deep learning methodology on mobile platform

A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm

Skin cancer is a risky ailment that can be effectively managed if detected promptly. However, timely recognition of skin cancer remains a challenge. In this study, a robotic computer-assisted tactic is proposed for the early detection of skin cancer. The motivation behind this research is to improve the accuracy and efficiency of skin cancer recognition. The proposed methodology involves a series of steps. Initially, the input images undergo preprocessing to enhance their quality and extract relevant features. These preprocessed images are then fed into a Gated Recurrent Unit (GRU) Network, a type of deep learning model known for its ability to capture sequential information. To optimize the performance of the GRU Network, we employ an enhanced variant of the Orca Predation Algorithm (OPA). This algorithm helps fine-tune the network parameters, improving its diagnostic capabilities. To validate and evaluate the effectiveness of our skin cancer diagnosis algorithm, we conducted experiments using the HAM10000 dataset, which contains a large collection of skin lesion images. We compared the results obtained from our proposed method, named GRU/IOPA, with eight existing techniques commonly used for skin cancer diagnosis. Five execution indices, namely sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were used to assess the performance of these methods. Our findings demonstrate that the GRU/IOPA system outperforms other existing methods in terms of sensitivity (0.95), specificity (0.97), PPV (0.95), NPV (0.96), and accuracy. These results indicate the effectiveness of our proposed method in diagnosing skin cancer compared to traditional approaches. The superior performance of GRU/IOPA highlights its potential impact on improving skin cancer diagnosis and reinforces its promise as an advanced tool in the field. In conclusion, our study presents a novel approach for timely skin cancer recognition through a robotic computer-assisted tactic. By utilizing the GRU/IOPA system, we achieve superior accuracy and efficiency in diagnosing skin cancer compared to existing techniques. This research offers significant contributions to the field of skin cancer diagnosis and opens up new avenues for future advancements in this area.

Skin Cancer Awareness and Knowledge About Sun Protection Practices in Solid Organ Transplant Recipients: Patient Survey Study.

Adequate sun protection practices in chronically immunosuppressed patients can minimize the burden of the most common type of skin cancer in this population. In addition, early recognition of skin cancer by patients can lead to decreased morbidity, and possibly mortality from the disease. Nevertheless, there are significant gaps in the knowledge of sun protection measures and early recognition of skin cancer. The aim of this study is to determine the risk factors of solid organ transplant recipients (SOTRs) for developing skin cancer and their sun exposure education and behavior post-transplantation. This study evaluates the responses of 107 SOTRs on their outlooks and beliefs of sunscreen usage, skin cancer, and sun exposure knowledge. Our study identified several significant risk factors for the development of actinic keratosis or keratinocyte carcinoma in SOTRs including history of sunburn before age 18, blue eyes, history of tanning bed use, performing monthly skin exams, ability to identify precancerous skin lesions, and history of previous skin examinations. A patient-centered approach needs to be used to properly educate patients on effective ways to reduce excessive sun exposure. Regular skin examinations, and patients continued education are necessary components in reducing the burden of skin cancer in SOTRs.

Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model.

Internet of Things (IoT)-assisted skin cancer recognition integrates several connected devices and sensors for supporting the primary analysis and monitoring of skin conditions. A preliminary analysis of skin cancer images is extremely difficult because of factors such as distinct sizes and shapes of lesions, differences in color illumination, and light reflections on the skin surface. In recent times, IoT-based skin cancer recognition utilizing deep learning (DL) has been used for enhancing the early analysis and monitoring of skin cancer. This article presents an optimal deep learning-based skin cancer detection and classification (ODL-SCDC) methodology in the IoT environment. The goal of the ODL-SCDC technique is to exploit metaheuristic-based hyperparameter selection approaches with a DL model for skin cancer classification. The ODL-SCDC methodology involves an arithmetic optimization algorithm (AOA) with the EfficientNet model for feature extraction. For skin cancer detection, a stacked denoising autoencoder (SDAE) classification model has been used. Lastly, the dragonfly algorithm (DFA) is utilized for the optimal hyperparameter selection of the SDAE algorithm. The simulation validation of the ODL-SCDC methodology has been tested on a benchmark ISIC skin lesion database. The extensive outcomes reported a better solution of the ODL-SCDC methodology compared with other models, with a maximum sensitivity of 97.74%, specificity of 99.71%, and accuracy of 99.55%. The proposed model can assist medical professionals, specifically dermatologists and potentially other healthcare practitioners, in the skin cancer diagnosis process.

Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation

We aimed to develop an accurate and efficient skin cancer classification system using deep-learning technology with a relatively small dataset of clinical images. We proposed a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet's deep convolutional neural networks were trained using a dataset of 1215 clinical images of skin tumors diagnosed at Taichung and Taipei Veterans General Hospital between 2015 and 2020. The dataset comprised five categories: benign nevus, seborrheic keratosis, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. The SkinFLNet's performance was evaluated using 463 clinical images between January and December 2021. SkinFLNet achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%, outperforming other deep convolutional neural network models. We also compared SkinFLNet's performance with that of three board-certified dermatologists, and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. Our study presents an efficient skin cancer classification system utilizing model fusion and lifelong learning technologies that can be trained on a relatively small dataset. This system can potentially improve skin cancer screening accuracy in clinical practice.

An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

AbstractSkin cancer is the most common deadly disease caused due to abnormal and uncontrolled growth of cells in the human body. According to a report, annually nearly one million people are affected by skin cancer worldwide. To protect human lives from such life‐threatening diseases, early identification of skin cancer is the only precautionary measure. In recent times, there already exist numerous automated techniques to detect and classify skin lesion malignancies using dermoscopic images. However, analyzing the dermoscopic images becomes an arduous task due to the presence of troublesome features such as light reflections, illumination variations, and uneven shape and dimension. To address the challenges faced during skin cancer recognition process, in this paper, we proposed an efficient intelligent automated system to detect and discriminate the dermoscopic images into malignant or benign. The proposed skin cancer detection model utilizes the HAM10000 dataset for evaluation. The dermoscopic images acquired from the HAM10000 dataset are initially preprocessed to enhance the quality of image and thus making it fit to train the classifier. Afterward, the most significant image patterns are extracted by the AlexNet architecture without any loss of detailed information. Later on, the extracted features are inputted to the proposed Improved Adaboost‐based Aphid–Ant Mutualism (IAB‐AAM) classification model to discriminate the images into malignant and benign categories. The proposed IAB‐AAM approach witnessed extensive enhancement in classification accuracy. The enhanced performance is attributed by integrating the AAM optimization concept with the IAB model. By comparing the performance of the proposed IAB‐AAM with other modern methods in terms of different evaluation indicators namely accuracy, precision, specificity, sensitivity, and f‐measure, the efficiency of the proposed IAB‐AAM technique is analyzed. From the experimental results, it is known that the proposed IAB‐AAM technique attains a greater accuracy rate of 95.7% in detecting skin cancer classes than other compared approaches.

DS02 British Society for Dermatological Surgery sustainability guidance

Abstract Skin surgery and the patient pathway for those with skin cancer significantly contribute to the climate crisis. Dermatological surgeons recognize the role of global climate change as a significant contributor to increasing skin cancer rates and hence increase service demand. The British Society for Dermatological Surgery (BSDS) sustainability subgroup’s aim is to facilitate the integration of sustainability within all levels of service provision through the development of national sustainability guidance. We systematically searched MEDLINE, Embase, Google Scholar and the grey literature for English-language published articles related to sustainability in skin surgery. Relevant selected evidence-based articles were critically evaluated for inclusion by 11 authors and, if sufficiently rigorous, were included in the final recommendations. The guidance then underwent multiple iterations of appraisal by the guidance development group consisting of expert consultant dermatologists and Mohs surgeons, trainees and patient–public involvement. A system-wide change in service provision is required to reduce long-term service demand. This can be achieved through a focus on skin cancer prevention, patient education and facilitating early recognition of skin cancer in primary care. Another aspect includes encouraging patient empowerment through self-monitoring, thus reducing the frequency of follow-up. Lean dermatology service delivery reduces waste by utilizing efficiency, optimizing teledermatology and one-stop see-and-treat clinics, reducing travel distances, using electronic health records and identifying unnecessary/missed appointments. Reducing carbon intensity involves using low-carbon alternatives for patient/staff transport, sustainable staff food options and a wider review of procurement and supply chain that reflects sustainable trade standards. Skin surgery must re-evaluate its consumption and waste, involving reusable equipment and surgical clothing, opting for repair over machinery replacement, reducing single-use sets and using absorbable sutures and single-wrapped biodegradable packaging. Departments should preserve water used in surgery/handwashing, conserve electricity and use renewable energy sources, in addition to surgical waste management through segregation, recycling, proper disposal of environmentally harmful waste and surgical smoke evacuation. Dermatological surgery departments can advocate system-wide change by establishing a sustainability policy, which incorporates departmental sustainability education for their staff, prioritizing waste auditing and life cycle assessments and encouraging ethical purchasing. Further skin surgery sustainability research and innovation are necessary to encourage circular economy solutions for consumables and equipment use and develop sustainability quality-improvement projects and new methods of sustainable dermatological surgery service delivery. The BSDS sustainability guidance is publicly available (https://bsds.org.uk/wp-content/uploads/2022/09/2022-BSDS_Sustainability_Guidance.pdf) to inform and inspire dermatological surgery sustainability.

Learning an artificial neural network to discover bit-quad-based formulas to compute basic object properties

Shape analysis requires estimating object properties in many applications, including optical character recognition, tumor classification, skin cancer recognition, leaf and plant identification, and cell analysis. In particular, bit-quad-based linear expressions proposed by Gray and Duda for calculating the area and perimeter of binary images are widely used in the literature. Nevertheless, these formulas require computing 14 or 15 bit-quad patterns out of 16 possible, becoming critical in applications with limited computing resources. Hence, this paper introduces a method based on a single-layer artificial neural network (ANN) to discover new expressions to calculate the area and perimeter with fewer bit-quads than the original formulas without losing measuring accuracy. Besides, an iterative elimination process removes irrelevant bit-quads whose corresponding weights approach zero. After that, an inductive analysis from observing the learned weights provides interpretable formulas for the area and perimeter. Furthermore, the proposed approach is also applied to find bit-quad-based formulas for directly computing the contact perimeter property, whose original formula requires precomputing Gray’s area and perimeter of the object. In addition, aiming to show our method’s versatility in other applications, we address a real-world problem to discover a bit-quad-based formula to distinguish between two classes of loss of bone density caused by hyperthyroidism and aging in rats. The experimental results show that the proposed approach reduces by approximately half the bit-quads needed to calculate the area and perimeter of Gray and Duda. Likewise, the number of bit-quads to compute the contact perimeter is reduced from nine to six. Besides, the estimated value on test sets by all the found formulas is the same as their original counterparts. On the other hand, the classification of loss of bone density type using the found bit-quad-based formula reaches an accuracy of 100% in the test set. Therefore, the proposed method is an alternative to finding linear expressions with few bit-quads to measure basic object properties.

Optimized Deep CNN with Deviation Relevance-based LBP for Skin Cancer Detection: Hybrid Metaheuristic Enabled Feature Selection

Segmentation of skin lesions is a significant and demanding task in dermoscopy images. This paper proposes a new skin cancer recognition scheme, with: “Pre-processing, Segmentation, Feature extraction, Optimal Feature Selection and Classification”. Here, pre-processing is done with certain processes. The pre-processed images are segmented via the “Otsu Thresholding model”. The third phase is feature extraction, where Deviation Relevance-based “Local Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix (GLCM) features and Gray Level Run-Length Matrix (GLRM) features” are extracted. From these extracted features, the optimal features are chosen via Particle Updated WOA (PU-WOA) model. Subsequently, classification occurs via Optimized DCNN and NN to classify the skin lesion. To make the classification more precise, the DCNN is optimized by the introduced algorithm. The result has shown a higher accuracy of 0.998737, when compared with other extant models like IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes.

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey.

The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.