Early Diagnosis Of Skin Cancer Research Articles

Diagnosing skin cancer using social spider optimization (SSO) and error correcting output codes (ECOC) with weighted hamming distance

Skin cancer is a common disease resulting from genetic defects, and early detection is critical to improve treatment outcomes. Diagnostic programs that use computer aid especially those that use supervised learning are very useful in early diagnosis of skin cancer. This research therefore presents a new approach that integrates optimization methods with supervised learning to improve skin cancer diagnosis using machine vision approach. The presented method is initiated by data pre-processing that involves the removal of unnecessary data. Then, to segment the images, a combination of K-means clustering and social spider optimization technique is employed. The region of interest is then extracted from the segmented image and from this region a convolutional neural network extracts the significant features. To enhance the classification performance as compared with the standard classifiers, this research introduces a new concept of error correcting output codes coupled with a weighted Hamming distance in the group of gamma classifiers. The ability of the proposed approach in segmentation of skin lesions and classifying them was tested using samples from the ISIC-2017 and ISIC-2016 databases. The introduced method obtained state-of-the-art accuracy on both datasets (ISIC-2016: 97.10%, ISIC-2017: 95.17%). In particularly, the accuracy of the introduced approach for both these databases is at least 1.17% higher than the compared methods. This proves the high performance of the suggested method based on the usage of the convolutional neural networks for feature extraction and gamma classifiers with error correcting output codes for classification in skin cancer detection.

Time from Diagnostic Suspicion to In-Person Evaluation in Skin Cancer Using Teledermatology.

Introduction: Early diagnosis of skin cancer is crucial for improving prognosis. Teledermatology (TD) usage can optimize referrals and reduce waiting times. This study aims to evaluate waiting times at the critical referral nodes in teleinterconsultations that raised suspicion of skin malignancy in the Chilean TD platform of the public health care system. Materials and Methods: A cross-sectional observational study that analyzed asynchronous teleinterconsultations and raised suspicion for skin malignancy following the teledermatologist evaluation was uploaded on the Chilean Ministry of Health's TD platform from January 1 to June 30, 2022. Results: Out of 20,522 teleinterconsultations, 1,853 raised suspicion of skin cancer. Among them, 1,119 patients were assessed by in-person examination, while 669 were still on the waiting list. Response times averaged 3.98 days for TD diagnostic suggestions. Overall referral times averaged 75.98 days from initial teleinterconsultation to the final specialist in-person evaluation. Waiting times showed significant differences among health care services and geographic regions. Discussion: In resource-limited settings, TD serves as a valuable tool to optimize referrals and manage the demand for oncologic dermatological consultation. The long waiting times emphasize the need for targeted interventions, especially in regions with longer delays. Conclusion: While TD has shown to be an effective tool in optimizing referrals, waiting times still exceed international recommendations, even in urban centers. The considerable heterogeneity in referral times within health care services and geographic regions highlights the necessity of establishing standardized referral protocols and explicit deadlines to fulfill teleinterconsultations that raise suspicion of skin malignancy in the Chilean public system.

A hybrid CNN with transfer learning for skin cancer disease detection.

The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the precise classification of skin lesions. However, dermatologists may find it difficult and time-consuming to accurately classify skin lesions. The use of transfer learning to boost skin cancer classification model precision is a promising strategy. In this work, we proposed a hybrid CNN with a transfer learning model and a random forest classifier for skin cancer disease detection. To evaluate the efficacy of the proposed model, it was verified over two datasets of benign skin moles and malignant skin moles. The proposed model is able to classify images with an accuracy of up to 90.11%. The empirical results and analysis assure the feasibility and effectiveness of the proposed model for skin cancer classification.

Melanoma Detection using Deep Learning Algorithms

Abstract: Melanoma is a type of skin cancer that arises from pigment-producing cells called melanocytes. These cells are responsible for the production of melanin, the pigment that gives skin, hair and eyes their color. Melanoma is considered more dangerous than other types of cancer because it can spread (metastasize) to other parts of the body. Key features of melanoma include the formation of abnormal or cancerous cells in the skin pigment. The main cause of melanoma is exposure to ultraviolet (UV) radiation from the sun or tanning beds. Most malignant tumours can be treated if diagnosed early. Therefore, early diagnosis of skin cancer is important to save the lives of patients. With today's technology, skin cancer can be diagnosed early and treated effectively. The traditional method of detecting melanoma relies on eye exams where dermatologists review biopsy results, which is time-consuming. These facilities are less available in rural areas where doctors are often available but dermatologists are not. This study was designed to develop a system that uses deep learning techniques to identify melanoma.

Segmentation of skin lesion using superpixel guided generative adversarial network with dual-stream patch-based discriminators

Accurate skin lesion segmentation in dermoscopic images is crucially important for the early diagnosis of skin cancer. Nevertheless, the existence of complex interfering factors, such as natural and artificial artifacts (e.g., hair and air bubbles), irregular appearance (e.g., varied shapes and low contrast), and significant differences in color shades cause the segmentation of skin lesion challenging. In this study, we propose a superpixel-guided generative adversarial network (GAN) with dual-stream patch-based discriminators for segmentation of the skin lesion. Specifically, in the designed GAN, a new multi-scale context extraction module (MCEM) is designed in the mask generator to enrich contextual information and capture boundary features of the lesion region, and thus accurately locating the object boundary of the lesion. Meantime, another branch of superpixel guided discriminator is designed and added into the discriminator module, which supplies so more compact and semantic information that enhances the discriminative ability. By using the dual-branch patch-based discriminators, the fine-grained discriminative power to local segmentation details is further enhanced and in turn making the generator produce more accurate segmentation masks. Comprehensive experiments show that our presented model achieves significant segmentation performance on the ISIC2016, ISIC2018, and HAM10000 skin lesion challenge datasets, and outperforms several promising deep convolutional neural networks.

Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging

Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments’ development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks.

The incidence of skin cancer is rising globally, posing a significant public health threat. An early and accurate diagnosis is crucial for patient prognoses. However, discriminating between malignant melanoma and benign lesions, such as nevi and keratoses, remains a challenging task due to their visual similarities. Image-based recognition systems offer a promising solution to aid dermatologists and potentially reduce unnecessary biopsies. This research investigated the performance of four unified convolutional neural networks, namely, YOLOv3, YOLOv4, YOLOv5, and YOLOv7, in classifying skin lesions. Each model was trained on a benchmark dataset, and the obtained performances were compared based on lesion localization, classification accuracy, and inference time. In particular, YOLOv7 achieved superior performance with an Intersection over Union (IoU) of 86.3%, a mean Average Precision (mAP) of 75.4%, an F1-measure of 80%, and an inference time of 0.32 s per image. These findings demonstrated the potential of YOLOv7 as a valuable tool for aiding dermatologists in early skin cancer diagnosis and potentially reducing unnecessary biopsies.

SASAN: ground truth for the effective segmentation and classification of skin cancer using biopsy images.

Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions. This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features. Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases. This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes.

Crow search algorithm with deep transfer learning driven skin lesion detection on dermoscopic images

The early diagnosis of skin cancer is of paramount importance for effective patient treatment. Dermoscopy, a non-surgical technique, utilizes precise equipment to examine the skin and plays a crucial role in identifying specific features and patterns that may indicate the presence of skin carcinoma. In recent times, machine learning (ML) methods have been developed to recognize and classify dermoscopic images as either malignant or benign. Deep learning (DL) systems, including Convolutional Neural Networks (CNNs), as well as various ML models like Random Forest (RF) classifiers and Support Vector Machine (SVM), are employed to extract relevant features from these images. This study introduces the Crow Search Algorithm with Deep Transfer Learning Driven Skin Lesion Detection on Dermoscopic Images (CSADTL-SLD) technique. The CSADTL-SLD method starts with the application of a median filter (MF) to remove noise from the images and utilizes the GoogleNet model for feature extraction. GoogleNet is well-regarded for its capacity to capture intricate and meaningful patterns within the data, which are essential for accurate lesion characterization. Furthermore, the CSADTL-SLD technique applies the Crow Search Algorithm (CSA) for parameter tuning of the GoogleNet model. After feature selection, the system employs the MLP classification model for precise lesion categorization. The comprehensive results of this research demonstrate the superiority of the CSADTL-SLD algorithm, showing significant enhancements in skin lesion detection accuracy and robustness when compared to existing methods. This approach holds promise as an effective solution for automating the detection and classification of skin lesions in dermoscopic images.

Artículo traducido] Impacto de la pandemia COVID-19 en el carcinoma de células escamosas cutáneo: un estudio unicéntrico de los factores epidemiológicos, clínicos e histopatológicos

BackgroundThe COVID-19 pandemic may have adversely affected the early diagnosis of skin cancer. ObjectiveTo compare epidemiological, clinical and histopathological characteristics in patients undergoing cutaneous squamous cell carcinoma (SCC) surgery before and after the beginning of the pandemic. Material & methodsWe conducted a cross-sectional study including two case series: (1) patients operated on for SCC in the year after the first state of alarm in Spain (15 March 2020), and (2) patients with SCC operated on in the previous year. Epidemiological, clinical and histopathological variables, tumour stage and risk grade were collected. Results248 patients were included (127 undergoing surgery before the pandemic and 121 after the pandemic). After the beginning of the pandemic, the percentage of high-risk SCC significantly increased from 35.3% to 46.2% (p=0.011). However, no significant differences were found in thickness, perineural invasion or metastases. ConclusionsAlthough there has not been a significant reduction in the number of SCC operated on after the pandemic, there has been a significant increase in high-risk SCC. All this could lead to an increase in skin cancer mortality in the future.

Transformer-based multi-attention hybrid networks for skin lesion segmentation

High-precision segmentation of skin lesions is essential for early diagnosis of skin cancer and improved patient survival. However, this task becomes challenging due to the irregularity of skin lesion regions, significant differences in color shades, ambiguity of boundaries, and other complex interfering factors. In this study, a transformer-based multi-attention hybrid network, TMAHU-Net, is proposed to cope with the complexity of skin lesion regions. The model introduces an innovative hybrid module that fully combines the advantages of CNNs and transformers to capture both global and local feature information. We also employ a deeply separable convolutional attention module to dynamically assign attention weights, which enhances the channel and spatial dimensions of feature information. To improve contextual relevance, we introduce an extended gated external attention module. Finally, a multi-scale aggregation module is used to fuse feature information at different scales to compensate for the differences in processing images with different resolutions. The model performs well in dealing with irregular, color shades, fuzzy boundaries, and complex interference lesion regions, which provides strong support for doctors to accurately determine complex lesion regions. Critically evaluated on three publicly available datasets, ISIC2016, ISIC2017, and PH2, TMAHU-Net achieved significant improvements over state-of-the-art methods, with F1 scores and IoU scores increasing by 1.01% and 1.01%, 1.22% and 1.40%, and 0.92% and 1.50%, respectively.

Impact of COVID-19 Pandemic on Cutaneous Squamous Cell Carcinoma: A Single-Centre Study of Epidemiologic, Clinic and Histopathological Factors

BackgroundThe COVID-19 pandemic may have adversely affected the early diagnosis of skin cancer. ObjectiveTo compare epidemiological, clinical and histopathological characteristics in patients undergoing cutaneous squamous cell carcinoma (SCC) surgery before and after the beginning of the pandemic. Material & methodsWe conducted a cross-sectional study including two case series: (1) patients operated on for SCC in the year after the first state of alarm in Spain (15 March 2020), and (2) patients with SCC operated on in the previous year. Epidemiological, clinical and histopathological variables, tumour stage and risk grade were collected. Results248 patients were included (127 undergoing surgery before the pandemic and 121 after the pandemic). After the beginning of the pandemic, the percentage of high-risk SCC significantly increased from 35.3% to 46.2% (p=0.011). However, no significant differences were found in thickness, perineural invasion or metastases. ConclusionsAlthough there has not been a significant reduction in the number of SCC operated on after the pandemic, there has been a significant increase in high-risk SCC. All this could lead to an increase in skin cancer mortality in the future.

Skin cancer diagnosis: Leveraging deep hidden features and ensemble classifiers for early detection and classification

ProblemCancer is a deadly disease that requires better diagnostics. Early detection improves skin cancer survival. Due to skin lesion dissimilarity, automated image categorization for skin cancer is problematic, making early diagnosis costly and complicated. ObjectiveThis study proposes a deep hidden feature and ensemble classifier skin cancer detection and classification technique. It handles real-time data streaming prediction and dimensionality issues. MethodsThe research work employs the sand cat swarm optimization with ResNet50 (SCSO-ResNet50) method to separate deep hidden features from known features, ensuring accurate predictions. They utilize an improved harmony search (EHS) approach to optimize features and reduce data dimensionality. Ensemble classifiers, including Naive Bayes, random forest, k-nearest neighbor (k-NN), support vector machine (SVM), and linear regression, are used for early cancer diagnosis. The performance of the proposed methodology is evaluated using the Kaggle skin cancer dataset and the ISIC 2019 dataset, comparing it to state-of-the-art classifiers in terms of accuracy, precision, recall, and F-measure. ResultsIn the second layer of skin cancer classification, the ensemble classifiers, baseline classifier, and CNN outperformed each other, improving prediction accuracy. ConclusionThe proposed methodology improved prediction accuracy compared to state-of-the-art classifiers, as evaluated on benchmark datasets. These findings highlight the potential of the suggested approach in enhancing the early diagnosis of skin cancer.

Automatic Detection and Categorization of Skin Lesions for Early Diagnosis of Skin Cancer Using YOLO-v3 - DCNN Architecture

Malignant melanoma is a type of benign skin cancer that is the most lethal due to its rapid development and affects a large number of people worldwide. Also, it is one of the deadliest diseases in the world. Moreover, existing research has stated that risk factors may be significantly decreased by making it nearly curable if diagnosed early on. This prompt identification and categorization necessitate using an automated system, even though the existing method is rather difficult. Hence our research employs the YOLO v3 - DCNN architecture to discover and categorize the deadliest kinds of skin cancer. Initially, YOLO v3 generates the feature map; simultaneously, colour features are extracted using colour moments with QuadHistogram, whereas Grey Level Co-occurrence Matrix (GLCM) with Redundant Contourlet Transform(RCT) generated texture features, and both (colour and texture) features get fused. Then, fused features are fed into the Deep Convolutional Neural Network (DCNN), which classifies the different types of skin cancer. Finally, our proposed approach is compared with the current works. Consequently, our proposed YOLO-v3 –DCNN has greater accuracy when contrasted with the baseline techniques.

Community outreach in vulnerable populations: Skin cancer screening for firefighters.

e21587 Background: Firefighters have both an increased risk (21%) and 20-year younger median age (42) at diagnosis of melanoma compared to the general population. The World Health Organization classifies firefighters’ occupational exposures as "carcinogenic to humans," but there is limited knowledge regarding sun protection habits, skin cancer history, and community outreach in this at-risk population. Methods: Free, full-body skin examinations conducted by board-certified dermatologists using the American Academy of Dermatology’s SPOT Skin CancerTM screening form were offered to firefighters from 26 cities and towns in Massachusetts on two separate days in August 2021 and October 2022. Data from the screening form and subsequent skin biopsy results were obtained and reported. Results: Of 195 firefighters screened, 30 (15.4%) were referred for biopsy. All were white males, mean age was 44.7 years, 26.7% had prior tanning bed exposure, 80% had prior blistering sunburns, 20% used sunscreen regularly, 86.7% did not have a regular dermatologist, and 6.7% had a history of keratinocyte-based skin cancer. Nineteen (63.3%) had biopsies, most frequently on the back or face, which resulted in three nodular basal cell carcinomas and 11 dysplastic nevi (seven moderate-to-severe). Conclusions: Firefighters are an underrecognized vulnerable population for the development of skin cancer. Community outreach in the form of free skin cancer screenings is a successful model for increasing awareness and early diagnosis of skin cancer among this population. In addition to providing access to care, community screenings offer tailored educational opportunities that promote skin cancer prevention in this high-risk group.

Multi-Models of Analyzing Dermoscopy Images for Early Detection of Multi-Class Skin Lesions Based on Fused Features

Melanoma is a cancer that threatens life and leads to death. Effective detection of skin lesion types by images is a challenging task. Dermoscopy is an effective technique for detecting skin lesions. Early diagnosis of skin cancer is essential for proper treatment. Skin lesions are similar in their early stages, so manual diagnosis is difficult. Thus, artificial intelligence techniques can analyze images of skin lesions and discover hidden features not seen by the naked eye. This study developed hybrid techniques based on hybrid features to effectively analyse dermoscopic images to classify two datasets, HAM10000 and PH2, of skin lesions. The images have been optimized for all techniques, and the problem of imbalance between the two datasets has been resolved. The HAM10000 and PH2 datasets were classified by pre-trained MobileNet and ResNet101 models. For effective detection of the early stages skin lesions, hybrid techniques SVM-MobileNet, SVM-ResNet101 and SVM-MobileNet-ResNet101 were applied, which showed better performance than pre-trained CNN models due to the effectiveness of the handcrafted features that extract the features of color, texture and shape. Then, handcrafted features were combined with the features of the MobileNet and ResNet101 models to form a high accuracy feature. Finally, features of MobileNet-handcrafted and ResNet101-handcrafted were sent to ANN for classification with high accuracy. For the HAM10000 dataset, the ANN with MobileNet and handcrafted features achieved an AUC of 97.53%, accuracy of 98.4%, sensitivity of 94.46%, precision of 93.44% and specificity of 99.43%. Using the same technique, the PH2 data set achieved 100% for all metrics.

A Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis

Skin cancer is one of the widespread diseases among existing cancer types. More importantly, the detection of lesions in early diagnosis has tremendously attracted researchers’ attention. Thus, artificial intelligence (AI)-based techniques have supported the early diagnosis of skin cancer by investigating deep-learning-based convolutional neural networks (CNN). However, the current methods remain challenging in detecting melanoma in dermoscopic images. Therefore, in this paper, we propose an ensemble model that uses the vision of both EfficientNetV2S and Swin-Transformer models to detect the early focal zone of skin cancer. Hence, we considerthat the former architecture leads to greater accuracy, while the latter model has the advantage of recognizing dark parts in an image. We have modified the fifth block of the EfficientNetV2S model and have included the Swin-Transformer model. Our experiments demonstrate that the constructed ensemble model has attained a higher level of accuracy over the individual models and has also decreased the losses as compared to traditional strategies. The proposed model achieved an accuracy score of 99.10%, a sensitivity of 99.27%, and a specificity score of 99.80%.

SkinNet‐ENDO: Multiclass skin lesion recognition using deep neural network and Entropy‐Normal distribution optimization algorithm with ELM

AbstractThe early diagnosis of skin cancer through clinical methods reduces the human mortality rate. The manual screening of dermoscopic images is not an efficient procedure; therefore, researchers working in the domain of computer vision employed several algorithms to classify the skin lesion. The existing computerized methods have a few drawbacks, such as low accuracy and high computational time. Therefore, in this work, we proposed a novel deep learning and Entropy‐Normal Distribution Optimization Algorithm with extreme learning machine (NDOEM)‐based architecture for multiclass skin lesion classification. The proposed architecture consists of five fundamental steps. In the first step, two contrast enhancement techniques including hybridization of mathematical formulation and convolutional neural network are implemented prior to data augmentation. In the second step, two pre‐trained deep learning models, EfficientNetB0 and DarkNet19, are fine‐tuned and retrained through the transfer learning. In the third step, features are extracted from the fine‐tuned models and later the most discriminant features are selected based on novel Entropy‐NDOELM algorithm. The selected features are finally fused using a parallel correlation technique in the fourth step to generate the result feature vectors. Finally, the resultant features are again down‐sampled using the proposed algorithm and the resultant features are passed to the extreme learning machine (ELM) for the final classification. The simulations are conducted on three publicly available datasets as HAM10000, ISIC2018, and ISIC2019 to achieving an accuracy of 95.7%, 96.3%, and 94.8% respectively.

SL-HarDNet: Skin lesion segmentation with HarDNet.

Automatic segmentation of skin lesions from dermoscopy is of great significance for the early diagnosis of skin cancer. However, due to the complexity and fuzzy boundary of skin lesions, automatic segmentation of skin lesions is a challenging task. In this paper, we present a novel skin lesion segmentation network based on HarDNet (SL-HarDNet). We adopt HarDNet as the backbone, which can learn more robust feature representation. Furthermore, we introduce three powerful modules, including: cascaded fusion module (CFM), spatial channel attention module (SCAM) and feature aggregation module (FAM). Among them, CFM combines the features of different levels and effectively aggregates the semantic and location information of skin lesions. SCAM realizes the capture of key spatial information. The cross-level features are effectively fused through FAM, and the obtained high-level semantic position information features are reintegrated with the features from CFM to improve the segmentation performance of the model. We apply the challenge dataset ISIC-2016&PH2 and ISIC-2018, and extensively evaluate and compare the state-of-the-art skin lesion segmentation methods. Experiments show that our SL-HarDNet performance is always superior to other segmentation methods and achieves the latest performance.

Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network

Skin cancer has been one of the major worldwide public health issue with more than 1 million cases every year. Skin cancer is classified into three categories: Basal Cell Carcinoma, Melanoma and Squamous Cell Carcinoma [1]. Melanoma is the most critical category of skin cancer with very thin chances of recovery and survival of the patient. Early diagnosis of skin cancer can drastically improve survival rate to as high as 95 percent. This served as a motivation to contribute to this noble cause using technology based solutions. In the process of diagnosis for the disease, the process is divided into four basic components: image processing including hair removal, noise removal, sharpening, and increasing the resolution of the image given to the skin dimension. Recent developments in identification of skin cancer technology uses machine learning and in-depth based reading segmentation algorithms. Most used algorithms are: InceptionV3, ResNet, VGGNet. This paper suggests an artificial skin cancer screening process using techniques like image processing and machine learning. Image super-resolution (ISR) techniques recreate an image with high resolution or sequence from visual LR images. An approach using deep learning on the Image super resolution was used to boost the accuracy of the convolutional neural network model. This model was developed using the Keras backend and tested the model by modifying the layers of neural network which are used for training. The model is built on publicly sourced dataset from the ISIC data archives.