A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.

Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

The Value of Excipients and the Required Understanding of the Biological System in Product Development: An Impactful Example of Curaderm, a Topical Skin Cancer Treatment

The incidences of nonmelanoma skin cancer are increasing worldwide, and the ongoing war on its treatment necessitates the development of effective and non-invasive methods. Through basic and clinical research, non-invasive treatments like Curaderm have been developed, leading to improved quality of life for patients. Excipients, previously considered inactive ingredients, play a crucial role in enhancing the performance of topical formulations. The development of Curaderm emphasizes the importance of understanding the interactions between active ingredients, excipients, and the biological system to create effective and affordable pharmaceutical formulations. The systematic approach taken in the development of Curaderm, starting from the observation of the anticancer activity of natural solasodine glycosides and progressing through toxicological and efficacy studies in cell culture, animals, and humans, has provided insights into the pharmacokinetics and pharmacodynamics of solasodine glycosides. It is crucial to determine these pharmacological parameters within the skin’s biological system for maximal effectiveness and cost-effectiveness of a skin cancer treatment. Curaderm, as a topical treatment for nonmelanoma skin cancer, offers benefits beyond those obtained from other topical treatments, providing hope for improved quality of life for patients.

Unique Clinical Features of Curaderm when Treating Skin Cancers

Basal cell carcinoma is the most common form of skin cancer and the most frequently occurring form of all cancers. Conventional treatments to remove or destroy basal cell carcinoma are indiscriminate and also remove or destroy normal skin cells resulting in compromised cosmetic outcomes. Consequences of these treatments include body-image issues, anxiety, post-traumatic stress disorder, depression, and poorer quality of social and family life. A progressive topical cream formulation, Curaderm, containing the natural BEC glycoalkaloids, have shown to have advantages over conventional treatments. However, comprehensive clinical features of the skin cancer lesions during treatment with Curaderm have to date not been reported. This report shows that using unpublished data from a large number of patients with varying sizes, types and locations of basal cell carcinomas when treated with Curaderm in a phase 3 trial, an initial increase in size of the lesions occur, followed by a reverse course, leading to complete removal of the skin cancer. The specificity and mode of action of Curaderm explains the superior cosmetic outcomes when compared with conventional therapies.

Gold Standard for Skin Cancer Treatment: Surgery (Mohs) or Microscopic Molecular-Cellular Therapy (Curaderm)?

Non-melanoma skin cancers or keratinocyte cancers such as basal cell carcinoma and squamous cell carcinoma make up approximately 80% and 20% respectively, of skin cancers with the 6 million people that are treated annually in the United States. 1 in 5 Americans and 2 in 3 Australians develop skin cancer by the age of 70 years and in Australia it is the most expensive, amassing $1.5 billion, to treat cancers. Non-melanoma skin cancers are often self-detected and are usually removed by various means in doctors’ surgeries. Mohs micrographic surgery is acclaimed to be the gold standard for the treatment of skin cancer. However, a novel microscopic molecular-cellular non-invasive topical therapy described in this article, challenges the status of Mohs procedure for being the acclaimed gold standard.

Smart MobiNet:A Deep Learning Approach for Accurate Skin Cancer Diagnosis

The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.

Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

Application of negative pressure wound therapy after skin grafting in the treatment of skin cancer:A case report

BACKGROUND Skin cancer is a common malignant tumor in dermatology.A large area must be excised to ensure a negative incisal margin on huge frontotemporal skin cancer,and it is difficult to treat the wound.In the past,treatment with skin grafting and pressure dressing was easy to cause complications such as wound infections,subcutaneous effusion,skin necrosis,and contracture.Negative pressure wound therapy(NPWT)has been applied to treat huge frontotemporal skin cancer.CASE SUMMARY Herein,we report the case of a 92-year-old woman with huge frontotemporal skin cancer.The patient presented to the surgery department complaining of ruptured bleeding and pain in a right frontal mass.The tumor was pathologically diagnosed as highly differentiated squamous cell carcinoma.The patient underwent skin cancer surgery and skin grafting,after which NPWT was used.She did not experience a relapse during the three-year follow-up period.CONCLUSION NPWT is of great clinical value in the postoperative treatment of skin cancer.It is not only inexpensive but also can effectively reduce the risk of surgical effusion,infection,and flap necrosis.

Skin Cancer Classification Using Transfer Learning by VGG16 Architecture (Case Study on Kaggle Dataset)

Skin cancer is a serious and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate diagnosis are critical for successful treatment and improved patient outcomes. In recent years, deep learning has emerged as a powerful tool for medical image analysis, including the diagnosis of skin cancer. The importance of using deep learning in diagnosing skin cancer lies in its ability to analyze large amounts of data quickly and accurately. This can help doctors make more informed decisions about patient care and improve overall outcomes. Additionally, deep learning models can be trained to recognize subtle patterns and features that may not be visible to the human eye, leading to earlier detection and more effective treatment. The pre-trained Visual Geometry Group 16 (VGG16) architecture has been used in this study to classification of skin cancer images, and the images have been converted into other color scales, there are named: 1) Hue Saturation Value (HSV), 2) YCbCr, 3) Grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 84.242%. The dataset has also been evaluated with other popular architectures and compared. The performance of VGG16 with images of each color scale is analyzed. In addition, feature parameters have been extracted from the different layers. The extracted layers were felt with the VGG16 to evaluate the ability of the feature parameters in classifying the disease.

Computer Decision Support System for Skin Cancer Localization and Classification

In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.

Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

The worldwide mortality rate due to cancer is second only to cardiovascular diseases.The discovery of image processing,latest artificial intelligence techniques,and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate.Efficiently applying these latest techniques has increased the survival chances during recent years.The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making.The datasets used for the experimentation and analysis are ISBI 2016,ISBI 2017,and HAM 10000.In this work pertained models are used to extract the efficient feature.The pertained models applied are ResNet,InceptionV3,and classical feature extraction techniques.Before that,efficient preprocessing is conducted on dermoscopic images by applying various data augmentation techniques.Further,for classification,convolution neural networks were implemented.To classify dermoscopic images on HAM 1000 Dataset,the maximum attained accuracy is 89.30%for the proposed technique.The other parameters for measuring the performance attained 87.34%(Sen),86.33%(Pre),88.44%(F1-S),and 11.30%false-negative rate(FNR).The class with the highest TP rate is 97.6%for Melanoma;whereas,the lowest TP rate was for the Dermatofibroma class.For dataset ISBI2016,the accuracy achieved is 97.0%with the proposed classifier,whereas the other parameters for validation are 96.12%(Sen),97.01%(Pre),96.3%(F1-S),and further 3.7%(FNR).For the experiment with the ISBI2017 dataset,Sen,Pre,F1-S,and FNR were 93.9%,94.9%,93.9%,and 5.2%,respectively.

Fluorescence lifetime imaging microscopy and its applications in skin cancer diagnosis

Fluorescence lifetime(FLT)of fluorophores is sensitive to the changes in their surrounding microenvironment,and hence it can quantitatively reveal the physiological characterization of the tissue under investigation.Fluorescence lifetime imaging microscopy(FLIM)provides not only morphological but also functional information of the tisse by producing spatially resolved image of fuorophore lifetime,which can be used as a signature of disorder and/or malignancy in diseased tissues.In this paper,we begin by introducing the basic principle and common detection methods of FLIM.Then the recent advances in the FLIM-based diagnosis of three different skin cancers,including basal cell carcinoma(BCC),squamous cell carcinoma(SCC)and malignant melanoma(MM)are reviewed.Furthermore,the potential advantages of FLIM in skin cancer diagnosis and the challenges that may be faced in the future are prospected.

Skin Cancer Treatment by Nanoskin Cellulose: Future Cancer Wound Healing

Cancer cells can be proliferating in a few months and years. It depends on cancer stage. Chemotherapy, immunotherapy and anti-metabolic drugs have been used in order to kill cancer cells and prevent immune system weakly and metastasis. However, such drugs can damage healthy cells too. Natural ways to cancer treatments may help whole body to cancer cells. In this work, it was taking off cancer nodule to skin cancer by surgery and we treat the nodule as wound, using Nanoskin® advance cell therapy (ACT), natural extra cellular matrix which releases nutrients to the skin cancer. Our result shows that the cancer nodule disappears in few weeks in skin, because of natural membrane treatment. In addition, we obtained complete wound healing due anticancer nutrients (beta-glucan) delivery to skin.

Diabetes and skin cancers: Risk factors, molecular mechanisms and impact on prognosis

Diabetes and skin cancers have emerged as threats to public health worldwide.However,their association has been less intensively studied.In this narrative review,we explore the common risk factors,molecular mechanisms,and prognosis of the association between cutaneous malignancies and diabetes.Hyperglycemia,oxidative stress,low-grade chronic inflammation,genetic,lifestyle,and environmental factors partially explain the crosstalk between skin cancers and this metabolic disorder.In addition,diabetes and its related complications may interfere with the appropriate management of cutaneous malignancies.Antidiabetic medication seems to exert an antineoplastic effect,however,future large,observation studies with a prospective design are needed to clarify its impact on the risk of malignancy in diabetes.Screening for diabetes in skin cancers,as well as close follow-up for the development of cutaneous malignancies in subjects suffering from diabetes,is warranted.

A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis

Melanoma or skin cancer is the most dangerous and deadliest disease.As the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer.In this study,a Hybrid Artificial Intelligence Model(HAIM)is designed for skin cancer classification.It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron(EWHMLP)for the classification.Though the wavelet transform is a powerful tool for signal and image processing,it is unable to detect the intermediate dimensional structures of a medical image.Thus the proposed HAIM uses Curvelet(CurT),Contourlet(ConT)and Shearlet(SheT)transforms as feature extraction techniques.Though MLP is very flexible and well suitable for the classification problem,the learning of weights is a challenging task.Also,the optimization process does not converge,and the model may not be stable.To overcome these drawbacks,EWHMLP is developed.Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33%in a multi-class approach on PH2 database.

A state of the science on influential factors related to sun protective behaviors to prevent skin cancer in adults

Skin cancer rates have risen over the past decades,making it imperative that adults understand the need for protection from sun exposure.Though some risk factors have been identified as predictive for skin cancers,there is a lack of synthesized information about factors that influence adults in their decisions to engage in sun protective behaviors.The purpose of this paper is to present the current state of the science on influential factors for sun protective behaviors in the general adult population.A rigorous literature search inclusive of a generally White,Caucasian,and non-Hispanic adult population was performed,and screening yielded 18 quantitative studies for inclusion in this review.Findings indicate that modifiable and non-modifiable factors are interdependent and play a role in sun protective behaviors.This study resulted in a proposed conceptual model for affecting behavioral change in sun protection including the following factors:personal characteristics,cognitive factors,family dynamics,and social/peer group influences.These factors are introduced to propose tailored nursing interventions that would change current sun protective behavior practice.Key implications for nursing research and practice focus on feasibility of annual skin cancer screening facilitated by advanced practice nurses,incorporating the identified influential factors to reduce skin cancer risk and unnecessary sun exposure.

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.

Clinical Effect of Photodynamic Therapy Combined Cryotherapy on Nonmelanoma Skin Cancer

The purpose of this study was to investigate the clinical efficacy of photodynamic combined freezing in patients with non-melanoma skin cancer(NMSC).First,according to the treatment regimen,96 patients with NMSC were divided into study group(n=50)and control group(n=46).The control group was treated with 5-amino-ketovalic acid photodynamic therapy(ALAPDT),while the study group was treated with ala-PDT combined with cryotherapy.Visual analogue scale(VAS)scores,visual satisfaction,clinical efficacy,adverse reactions,and progression-free survival were compared between the two groups.The results showed that VAS score in the study group was slightly higher than that in the control group,but the difference was not statistically significant(P>0.05).The appearance satisfaction and total effective rate of patients in the study group were higher than those in the control group,and the difference was statistically significant(P<0.05).The total incidence of adverse reactions in the study group was slightly higher than that in the control group,but the difference was not statistically significant(P>0.05).3 years progressionfree survival time and 3 years progression-free survival rate were compared between the two groups,and the difference was not statistically significant(P>0.05).Therefore,the combination of PDT and cryotherapy for non-melanoma skin cancer has a good clinical effect,which is conducive to the recovery of skin lesions,high patient satisfaction,fewer adverse reactions,and longer progression-free survival.In addition,the combined therapy can provide a new treatment idea for non-melanoma skin cancer patients who are not suitable for surgical treatment.

Clinical Effect and Tolerance Analysis of Photodynamic/Electroion in the Treatment of Skin Cancer and Precancerous Lesions

The study was dedicated to explore the clinical efficacy and tolerability of photodynamic/electroion therapy of skin cancer and precancerous lesions.Firstly,a total of 60 patients with skin cancer and precancerous lesions,who were admitted to the Dermatology Department of the Fifth Affiliated Hospital of Zhengzhou University from November 2013 to November 2016,were selected and divided into observation group and control group according to the random number table,with 30 patients in each group.Observation group adopted the photodynamic/electroion therapy,and control group adopted photodynamic therapy(PDT).Two groups of patients were last follow-up to December 2018,and loss of follow-up and death were taken as the end point events of follow-up.The frequency of treatment,clinical efficacy and severity of adverse reactions of the two groups were calculated,and Kaplan-Meier curves were plotted to compare the progression free survival(PFS)of the two groups.The results showed that the number of treatment times per capita in the observation group was 3.6±1.1 times,which was lower than that of in the control group(4.1±1.1 times),but the difference was not statistically significant(t=1.760,P=0.083).The total effective rate in the observation group(100.00%)was higher than that of in the control group(80.00%),and the difference was significant(P<0.05).In addition,during the treatment,both groups had adverse reactions such as burning,pain,redness,swelling and exudation of different degrees,but there was no significant difference in the severity score of adverse reactions(P>0.05).There was no statistically significant difference in the follow-up time and PFS between the two groups(P>0.05),but the recurrence rate of the observation group was significantly lower than that of the control group(P<0.05).Therefore,the clinical efficacy of photodynamic/electroion treatment in patients with skin cancer and precancerous lesions was superior to that of PDT alone,which increased the risk of tolerance.So,it is worthy of clinical promotion.

One-stage limb Pelnac®reconstruction after removal of skin cancer:safety,efficacy,and aesthetic outcomes

Objective To assesse the outcomes of one-stage limb reconstruction after removal of skin cancers defect.Methods This prospective study was conducted from September 2017 to January 2020 and included 15 patients.All patients underwent extensive tumor resection and one-stage Pelnac®reconstruction of large skin defects,and regular postoperative follow-up was scheduled.At the 6-month follow-up,tumor recurrence and scar quality was assessed using the Vancouver Scar Scale(VSS).None of the patients exhibited infection,wound necrosis,hematoma,seroma,or recurrence.Results All the skin grafts were well accepted by the patients.Nine patients reported normal or near-normal sensory function,while six reported slight sensory loss.No cases of significant functional loss were observed.We enrolled 10 men and 5 women with a mean age of 63.9 years(range:46-78 years).The mean follow-up duration was 20.6 months(range:12-36 months).The skin tumors were located on the feet(n=4),forearms(n=3),and legs(n=8).The malignant tumors included malignant melanomas(13.3%),basal cell carcinomas(33.3%),and squamous cell carcinomas(53.3%).The mean operative time was 40.7 min.Two patients underwent radiotherapy.The average length of hospital stay was 2.6 days.The mean skin defect area was 33.2 cm^(2)(range:16.6-51.6 cm^(2)).The patient satisfaction score(regarding the aesthetic appearance of the grafted area)was 79.7/100,and the VSS score was 3.8.Conclusion Pelnac®dermal templates facilitate efficient and reliable reconstruction of skin defects after skin cancer resection.