Early Diagnosis Of Skin Cancer Research Articles

Deep Convolutional Neural Networks for Skin Lesions Classification via Ensemble Strategy

The early diagnosis of skin cancer is an urgent problem in public health, which helps to improve the survival rate of patients. In order to improve the model generalization ability, we propose an ensemble model, which first uses multiple different neural network models to train each model on the training set and then integrates the output of different models to form a rich feature representation to classify skin lesions. The experimental results show that the proposed ensemble model is effective, and its performance is better than the single model, with an accuracy of 89.5%.

Detection of Skin Cancer using Convolutional Neural Network

Humanity faces a serious threat from skin cancer. Because of Melanoma skin cancer's quick development rate, high treatment costs, and high mortality rate, the importance of early skin cancer diagnosis has increased. Cancer cells are carefully found, and in most cases, treatment takes time. Using image processing and machine learning, this paper suggested an artificial skin cancer diagnosis system. After segmenting the dermoscopic pictures using the feature extraction approach, the characteristics of the damaged skin cells are retrieved. The retrieved features are stratified using a convolutional neural network classifier based on deep learning. After using the publicly accessible data set, an accuracy of 89.5 percent and a training accuracy of 93.7 percent were reached.

AS-Net: Attention Synergy Network for skin lesion segmentation

Accurate skin lesion segmentation in dermoscopic images is crucial to the early diagnosis of skin cancers. However, it remains a challenging task due to fuzzy lesion boundaries, irregular lesion shapes, and the existence of various interference factors. In this paper, a novel Attention Synergy Network (AS-Net) is developed to enhance the discriminative ability for skin lesion segmentation by combining both spatial and channel attention mechanisms. The spatial attention path captures lesion-related features in the spatial dimension while the channel attention path selectively emphasizes discriminative features in the channel dimension. The synergy module is designed to optimally integrate both spatial and channel information, and a weighted binary cross-entropy loss function is introduced to emphasize the foreground lesion region. Comprehensive experiments indicate that our proposed model achieves the state-of-the-art performance with the highest overall score in the ISIC2017 challenge, and outperforms several popular deep neural networks on both ISIC2018 and PH2 datasets.

Emergency Use and Efficacy of an Asynchronous Teledermatology System as a Novel Tool for Early Diagnosis of Skin Cancer during the First Wave of COVID-19 Pandemic.

Background: After the outbreak of the corona virus disease-19 (COVID-19) pandemic, teledermatology was implemented in the Hungarian public healthcare system for the first time. Our objective was to assess aggregated diagnostic agreements and to determine the effectiveness of an asynchronous teledermatology system for skin cancer screening. Methods: This retrospective single-center study included cases submitted for teledermatology consultation during the first wave of the COVID-19 pandemic. Follow-up of the patients was performed to collect the results of any subsequent personal examination. Results: 749 patients with 779 lesions were involved. 15 malignant melanomas (9.9%), 78 basal cell carcinomas (51.3%), 21 squamous cell carcinomas (13.8%), 7 other malignancies (4.6%) and 31 actinic keratoses (20.4%) were confirmed. 87 malignancies were diagnosed in the high-urgency group (42.2%), 49 malignancies in the moderate-urgency group (21.6%) and 16 malignancies in the low-urgency group (4.6%) (p < 0.0001). Agreement of malignancies was substantial for primary (86.3%; κ = 0.647) and aggregated diagnoses (85.3%; κ = 0.644). Agreement of total lesions was also substantial for primary (81.2%; κ = 0.769) and aggregated diagnoses (87.9%; κ = 0.754). Conclusions: Our findings showed that asynchronous teledermatology using a mobile phone application served as an accurate skin cancer screening system during the first wave of the COVID-19 pandemic.

LinkNet-B7: Noise Removal and Lesion Segmentation in Images of Skin Cancer

Skin cancer is common nowadays. Early diagnosis of skin cancer is essential to increase patients’ survival rate. In addition to traditional methods, computer-aided diagnosis is used in diagnosis of skin cancer. One of the benefits of this method is that it eliminates human error in cancer diagnosis. Skin images may contain noise such as like hair, ink spots, rulers, etc., in addition to the lesion. For this reason, noise removal is required. The noise reduction in lesion images can be referred to as noise removal. This phase is very important for the correct segmentation of the lesions. One of the most critical problems in using such automated methods is the inaccuracy in cancer diagnosis because noise removal and segmentation cannot be performed effectively. We have created a noise dataset (hair, rulers, ink spots, etc.) that includes 2500 images and masks. There is no such noise dataset in the literature. We used this dataset for noise removal in skin cancer images. Two datasets from the International Skin Imaging Collaboration (ISIC) and the PH2 were used in this study. In this study, a new approach called LinkNet-B7 for noise removal and segmentation of skin cancer images is presented. LinkNet-B7 is a LinkNet-based approach that uses EfficientNetB7 as the encoder. We used images with 16 slices. This way, we lose fewer pixel values. LinkNet-B7 has a 6% higher success rate than LinkNet with the same dataset and parameters. Training accuracy for noise removal and lesion segmentation was calculated to be 95.72% and 97.80%, respectively.

Skin Lesion Segmentation Using Recurrent Attentional Convolutional Networks

Accurate segmentation of lesion region from skin lesion images can provide guidance for medical experts to make an early diagnosis of skin cancer. In this study, we construct Recurrent Attentional Convolutional Networks (O-Net), which exploits the skin lesion’s attention class feature with a recurrent O-shape structure, in an iterative refinement strategy for skin lesion image segmentation. Inspired by the recently proposed attention class feature network, we integrate the attention class feature module into the proposed networks. The O-Net, with recurrent unit to iteratively refine the segmentation result, is designed to extract attention feature information and enable coarse-to-fine feature representation by iteratively integrating attention feature maps into network. Furthermore, O-Net calculates the attentional class feature by extracting attention information from the coarse segmentation result. Two currently popular datasets ISIC-2017 and PH2 are employed to explore the validity of our proposed model. The study provides detailed comparisons of our proposed network, the attention class feature network and Recurrent U-Net. O-Net achieved Dice coefficient by 87.04% on the ISIC-2017 dataset, 92.12% on the PH2 dataset with corresponding Jaccard indices of 80.36% and 86.15%, respectively, on the same dataset, which exhibits competitive performance for skin lesion image segmentation in results. The visual results also shown that more detailed tissues are extracted by O-Net than other methods.

EVALUATION OF THE KNOWLEDGE OF SUN DAMAGE, SOLAR PROTECTION AND SKIN CANCERS AMONG UNIVERSITY STUDENTS AND THEIR PARENTS

Purpose: Awareness of the harmful effects of the sun and the knowledge of protection, and awareness of the follow up of nevi, are important for the prevention and early diagnosis of skin cancers. Our aim is to evaluate the knowledge of university students and their parents about the harmful effects of the sun, and associated skin cancers. Methods: 653 university students and 615 parents participated in our cross-sectional study. The survey was delivered via the SurveyMonkey application link and demographic data was recorded. Fitzpatrick skin types, natural hair and eye colors, and personal and family skin cancer history were questioned. Information on the harmful effects of the sun, nevus examination, self-skin examination, symptoms of skin cancers and facilitating factors were questioned. Results: 1004 of the participants were women, 264 were men. Fitzpatrick II skin type and brown eyes were the most common in both groups. The nevi follow-up and self-examination habits did not differ statistically between the groups. Nevi follow-up was regarded as essential and requiring a dermatologist. Participants believed that the most common skin cancer was malignant melanoma. Conclusion: It is important to raise awareness about skin cancers, to follow up nevi and to introduce the habit of self-examination into society in general.

Reverse Iontophoretic Extraction of Skin Cancer-Related Biomarkers.

Non-invasive methods for early diagnosis of skin cancer are highly valued. One possible approach is to monitor relevant biomarkers such as tryptophan (Trp) and kynurenine (Kyn), on the skin surface. The primary aim of this in vitro investigation was, therefore, to examine whether reverse iontophoresis (RI) can enhance the extraction of Trp and Kyn, and to demonstrate how the Trp/Kyn ratio acquired from the skin surface reflects that in the epidermal tissue. The study also explored whether the pH of the receiver medium impacted on extraction efficiency, and assessed the suitability of a bicontinuous cubic liquid crystal as an alternative to a simple buffer solution for this purpose. RI substantially enhanced the extraction of Trp and Kyn, in particular towards the cathode. The Trp/Kyn ratio obtained on the surface matched that in the viable skin. Increasing the receiver solution pH from 4 to 9 improved extraction of both analytes, but did not significantly change the Trp/Kyn ratio. RI extraction of Trp and Kyn into the cubic liquid crystal was comparable to that achieved with simple aqueous receiver solutions. We conclude that RI offers a potential for non-invasive sampling of low-molecular weight biomarkers and further investigations in vivo are therefore warranted.

Hybrid Convolutional Neural Network Architectures for Skin Cancer Classification

Skin cancer is a common form of cancer seen in humans. Like other diseases, early diagnosis of skin cancer is vital. In the study, deep learning architectures, which are popular machine learning algorithms, are used to classify skin cancer. In order to increase accuracy performance, hybrid structures are realized using K-Nearest neighbor (KNN), Support vector machine (SVM) and Decision tree (DT). After feature extraction using convolutional neural network, KNN, SVM and DT are applied separately for classification. While the KNN and SVM of the produced hybrid structures increase performance, the use of the decision tree has negatively affected the performance. After the training and validation processes with the seven-class skin cancer mnist: ham10000 dataset containing dermatological images, the validation accuracy and confusion matrix criteria of the architectures are reported. Eight different architectures are implemented. The highest accuracy is provided by the structure in which the last layer of Alexnet architecture is replaced by the SVM classifier.

Big transfer learning for automated skin cancer classification

&lt;p&gt;Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.&lt;/p&gt;

Results of a 10-year web-based health promotion campaign against skin cancer in Switzerland.

Skin cancer is the most common malignancy with rising incidence. Although early detection can be lifesaving, prevention programmes are under-utilized. In 2008, a group of board-certified dermatologists in Switzerland established a website aimed at educating about skin cancer risk factors and providing guidance on self-assessment. To present the data of this programme over the last 10 years with regards to representation of the targeted groups and sustained impact on primary skin cancer prevention. A comprehensive web-based health promotion campaign was established for education and guidance on self-assessment. Teledermatological evaluation was offered and participants were then interviewed. In total, 11,171 digital photos were evaluated during 2008-2018; 54.3% (n = 6,067) from females and 45.7% (n = 5,104) from males. In 26.7% (n = 2,983), clinical examination was recommended. Of the participants, 1,874 replied revealing 103 malignancies (9.2% of the lesions were presented to a physician): 34 melanomas in situ, six squamous cell carcinomas, 53 basal cell carcinomas and 10 malignant lesions (not further specified). Of the participants, 40.5% (n = 683) changed their attitude towards sun exposure, 48.7% (n = 820) used more sunscreen, and 57.5% (n = 966) improved sunscreen measures. Web-based educational programmes raise public awareness, enhance prevention, and support early diagnosis of skin cancer. Teledermatology might contribute to reducing skin cancer mortality rates.

Aile Hekimlerinin Cilt Kanseri Farkındalıkları ve Güneşten Korunma Davranışları

Objective: The objective of this study was to determine the knowledge level of family physicians about skin cancer and their attitudes and behaviors about sun protection. Methods: The sample of this study was composed of 295 family physicians who worked in Samsun province and participated in in-service trainings organized by the Provincial Health Directorate in March 2019. A questionnaire form consisting of questions on sociodemographic characteristics, attitudes and behaviors about sun protection was applied to the participants. Result: A total of 295 family physicians, 64.7% male and 35.3% female, participated in the study. Most of the physicians (87.8%) expressed that they tried to protect from sunlight. Women (68.3%) used sunscreen lotions/creams more frequently than men (41.4%). Those who had 2 and more severe sunburns throughout their lifetime (51.5%) stated that they described themselves at higher risk than those who did not (36.2%) which was statistically significant (p&amp;lt;0.05). The reasons of the physicians for application to a dermatologist were: 47(15.9%) had asymmetrically shaped, irregularly structured moles larger than 5 mm, 37(12.5%) had suspected presence of moles, 11(3.7%) wanted whole body examination for moles, 20(6.8%) requested advice on daily skin care and prevention of skin aging. Conclusion: Awareness studies should be carried out on the harmful effects of the sun and early diagnosis of skin cancer especially for family physicians, healthcare professionals and other parts of the society. In addition, considering that visual evaluation has a role in the diagnosis of skin cancer, it will be useful for family physicians to inform their patients about the detrimental effects of the sun and self-skin examination.

Primary and secondary prevention of skin cancers

Skin neoplasms are among the more common malignant neoplasms in the human population. Due to the constantly increasing incidence of skin cancers, it is very important to diagnose them early, which at an early stage, especially in the case of malignant melanoma, gives a good therapeutic prognosis. The aim of the study was to draw attention to the need to use prophylaxis as an effective action in the fi eld of popularizing epidemiological knowledge and disseminating pro-health behaviors (primary prophylaxis). Activities enabling early diagnosis of skin cancer using clinical tests, algorithms and modern diagnostic methods such as dermatoscopy, confocal microscopy. and optical coherence tomography (secondary prophylaxis). Primary prophylaxis combined with secondary prophylaxis can reduce the incidence, morbidity and mortality of skin cancer. Control tests among people with an increased risk factor are an important element of the prophylaxis.

The role of drugs and selected dietary factors in cutaneous squamous cell carcinogenesis.

Cutaneous squamous cell carcinoma represents the second most common non-melanoma skin cancer and its incidence increases worldwide. This review provides an overview of selected exogenous risk factors for cutaneous squamous cell carcinoma, which include drugs (azathioprine, calcineurin inhibitors, hydrochlorothiazide, angiotensin-converting-enzyme inhibitors) and few dietary factors (fat meet, whole milk products, arsenic) to better understand squamous skin cancer etiopathogenesis. Ingredients such as leafy vegetables, nuts, fish, caffeine, niacin are preventive factors for cutaneous squamous cell cancer. The heart transplant recipients have an increased risk of squamous cell carcinoma development than kidney or liver transplant ones and switching photosensitizing azathioprine to mycophenolate mofetil can reduce the incidence of cutaneous squamous cell carcinoma. The great attention should be paid to early change of cardiac photosensitizing antihypertensive drugs to non-photosensitizing ones among patients with a history of prior skin cancers and among organ transplant recipients. Based on current knowledge that ultra-violet radiation is the main risk factor for squamous cell carcinoma development, promotion of the skin self-examination, photoprotection, tanning bed avoidance and early skin cancer diagnosis is important for this tumour prevention.

Dermal epidermal junction detection for full-field optical coherence tomography data of human skin by deep learning

Full-field optical coherence tomography (FF-OCT) has been developed to obtain three-dimensional (3D) OCT data of human skin for early diagnosis of skin cancer. Detection of dermal epidermal junction (DEJ), where melanomas and basal cell carcinomas originate, is an essential step for skin cancer diagnosis. However, most existing DEJ detection methods consider each cross-sectional frame of the 3D OCT data independently, leaving the relationship between neighboring frames unexplored. In this paper, we exploit the continuity of 3D OCT data to enhance DEJ detection. In particular, we propose a method for noise reduction of the training data and a multi-directional convolutional neural network to predict the probability of epidermal pixels in the 3D OCT data, which is more stable than one-directional convolutional neural network for DEJ detection. Our crosscheck refinement method also exploits the domain knowledge to generate a smooth DEJ surface. The average mean error of the entire DEJ detection system is approximately 6 μm.

A robust method for skin cancer diagnosis based on interval analysis

Early diagnosis of skin cancer from dermoscopy images significantly reduces the mortality due to this cancer. However, several reasons impact the system diagnosis precision. One of the important problems in this process happens during image acquisition. Often, in medical photography, there are some uncertainties like noises and brightness variations, initial digitalization and sampling which affect the image quality. This study presents a new approach for border detection of the cancer area by considering the uncertainties. Interval analysis is utilized to extend the proposed edge detection method and the Hukuhara method is utilized for developing the differentiation formula for edge detection in the interval space. Simulation results are applied to two different skin cancer atlas and the results are compared with three popular methods by considering two types of noises including Gaussian noise and salt-and-pepper noise. The results showed that the introduced method gives better results than the compared methods.

Inequalities in the patterns of dermoscopy use and training across Europe: conclusions of the Eurodermoscopy pan-European survey.

Dermoscopy is a widely used technique, recommended in clinical practice guidelines worldwide for the early diagnosis of skin cancers. Intra-European disparities are reported for early detection and prognosis of skin cancers, however, no information exists about regional variation in patterns of dermoscopy use across Europe. To evaluate the regional differences in patterns of dermoscopy use and training among European dermatologists. An online survey of European-registered dermatologists regarding dermoscopy training, practice and attitudes was established. Answers from Eastern (EE) versus Western European (WE) countries were compared and their correlation with their respective countries' gross domestic product/capita (GDPc) and total and government health expenditure/capita (THEc and GHEc) was analysed. We received 4,049 responses from 14 WE countries and 3,431 from 18 EE countries. A higher proportion of WE respondents reported dermoscopy use (98% vs. 77%, p<0.001) and training during residency (43% vs. 32%) or anytime (96.5% vs. 87.6%) (p<0.001) compared to EE respondents. The main obstacles in dermoscopy use were poor access to dermoscopy equipment in EE and a lack of confidence in one's skills in WE. GDPc, THEc and GHEc correlated with rate of dermoscopy use and dermoscopy training during residency (Spearman rho: 0.5-0.7, p<0.05), and inversely with availability of dermoscopy equipment. The rates and patterns of dermoscopy use vary significantly between Western and Eastern Europe, on a background of economic inequality. Regionally adapted interventions to increase access to dermoscopy equipment and training might enhance the use of this technique towards improving the early detection of skin cancers.

Computer-aided diagnosis of skin cancer based on soft computing techniques.

Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

Skin Cancer System Detection Using K-Means Algorithm

Melanoma is the type of skin cancer which will spread fast into the inner most layer of skin. The early detection of the skin tumor will avoid spreading of the disease, and also reduce the cancer cells. More methods are there to detect the harmful cancer present in the skin. Once the skin is infected by some disease, the patient should consult doctor and getting the suggestion from the doctor. The proposed work is used to early diagnosis of skin cancer at home before consulting doctor. The important aim in this proposed work is that it will find the cancer present in the skin using K-Means Algorithms. In this proposed system the input will be the image of the skin and the output will be the detection of the affected portion whether it is normal or abnormal. This proposed system helps us to save the time and gives the best result before consulting the doctor when compared with existing system.

Skin Cancer Classification Using Dermoscopic Images based on Ranklet Transform, Co-occurrence Features and Random Forest Classifier

The skin cell which grows abnormally is known as skin cancer and it is caused due to sun rays in the&#x0D; uncovered skin. It spreads within a week, so the early diagnosis of skin cancer is required. Skin Cancer&#x0D; Classification (SCC) based on ranklet transform, co-occurrence features and random forest classifier is&#x0D; presented in this paper. Dermoscopic image in the PH2 database is used in this study for performance&#x0D; evaluation. Initially, the dermoscopic images of three categories normal benign and malignant images are&#x0D; preprocessed to smooth the images. Then the images are given to ranklet transform for decomposition. It&#x0D; produces subband coefficients. The Ranklet Features based Co-occurrence Matrix (RFCM) is used to extract&#x0D; the features and stored in database. The classification is made by Random Forest Classifier (RFC). The result&#x0D; shows better classification accuracy of 93.5% sensitivity is 92% and specificity is 95% is obtained by RFCM&#x0D; and RFC.