Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.

Acral melanoma detection using dermoscopic images and convolutional neural networks

Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

Linear porokeratosis of the foot with dermoscopic manifestations: A case report

BACKGROUND Porokeratosis(PK)is a common autosomal dominant chronic progressive dyskeratosis with various clinical manifestations.Based on clinical manifestations,porokeratosis can be classified as porokeratosis of mibelli,disseminated superficial porokeratosis,disseminated superficial actinic porokeratosis,linear porokeratosis(LP),porokeratosis palmaris et plantaris disseminata,porokeratosis punctata,popular PK,hyperkeratosis PK,inflammatory PK,verrucous PK,and mixed types.We report a case of LP in a child and describe its dermoscopic findings.CASE SUMMARY Linear porokeratosis is a rare PK.The patient presented with unilateral keratinizing maculopapular rash of the foot in childhood.The patient underwent skin pathology and dermoscopy,and was treated with liquid nitrogen freezing and topical drugs.CONCLUSION From this case we take-away that LP is a rare disease,by the dermoscopic we can identify it.

Summarization and comparison of dermoscopic features on different subtypes of rosacea

Background:The dermoscopic features of rosacea have already been reported.However,the current findings are incomplete,and little is known about phymatous rosacea.Hence,this study aimed to summarize and compare the dermoscopic features and patterns of three rosacea subtypes(erythematotelangiectatic[ETR],papulopustular[PPR],and phymatous[PHR])in the Chinese Han population and to evaluate whether these features differ with patients’genders,ages,and durations.Methods:Dermoscopic images of 87 rosacea patients were collected in non-polarized and polarized dermoscopy contact modes at 20-fold magnification.Dermoscopic features,including vessels,scales,follicular findings,and other structures,were summarized and evaluated.Results::The reticular linear vessels and red diffuse structureless areas of ETR were distinctive.For PPR,red diffuse structureless areas,reticular linear vessels,yellow scales,follicular plugs,and follicular pustules were typical dermoscopic criteria.The common dermoscopic features of PHR were:orange diffuse structureless areas,linear vessels with branches,perifollicular white color,orange focal structureless areas,and white lines.The following features statistically differed among the three rosacea subtypes:reticular linear vessels(P<0.001),unspecific linear vessels(P=0.005),linear vessels with branches(P<0.001),yellow scales(P=0.001),follicular plugs(P<0.001),perifollicular white color(P<0.001),red diffuse structureless areas(P=0.022),orange diffuse structureless areas(P<0.001),red focal structureless areas(P=0.002),orange focal structureless areas(P=0.003),white lines(P<0.001),follicular pustules(P<0.001),and black vellus hairs(P<0.001).Conclusions:The dermoscopic patterns of ETR are red diffuse structureless areas and reticular linear vessels.For PPR,the pattern comprehends combinations of red diffuse structureless areas,reticular linear vessels,yellow scales,follicular plugs,and follicular pustules.Meanwhile,PHR is characterized by remarkable orange diffuse structureless areas,linear vessels with branches,perifollicular white color,orange focal structureless areas,and white lines.

Dermoscopic signs of subcorneal hematoma on the extremities

Introduction Subcorneal hematoma is most commonly seen on palms and soles. Its patterns seen on dermoscopy have been previously described[1-2], but dermoscopic signs of subcorneal hematoma on other sites were few reported. Here, we present two cases of subcorneal hematoma on the extremities with characteristic findings observed by dermoscopy.

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide,and the earlier identification of skin lesions using dermoscopic images can raise the survival rate.Classification of the skin lesion from those dermoscopic images will be a tedious task.The accuracy of the classification of skin lesions is improved by the use of deep learning models.Recently,convolutional neural networks(CNN)have been established in this domain,and their techniques are extremely established for feature extraction,leading to enhanced classification.With this motivation,this study focuses on the design of artificial intelligence(AI)based solutions,particularly deep learning(DL)algorithms,to distinguish malignant skin lesions from benign lesions in dermoscopic images.This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoen-coder(OSSAE)based feature extractor with backpropagation neural network(BPNN),named the OSSAE-BPNN technique.The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region.In addition,the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis.Moreover,the parameter tuning of the SSAE model is carried out by the use of sea gull optimization(SGO)algo-rithm.To showcase the enhanced outcomes of the OSSAE-BPNN model,a comprehensive experimental analysis is performed on the benchmark dataset.The experimentalfindings demonstrated that the OSSAE-BPNN approach outper-formed other current strategies in terms of several assessment metrics.

Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms

Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

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.

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

Factors influencing response to ingenol mebutate therapy for actinic keratosis of face and scalp

AIM To determine factors independently influencing response to ingenol mebutate therapy and assess efficacy on clinical setting of non-hypertrophic non-hyperkeratotic actinic keratosis (AK).METHODS Consecutive patients affected by non-hypertrophic non-hyperkeratotic AKs of the face or scalp were enrolled to receive ingenol mebutate 0.015% gel on a selected skin area of 25 cm^2 for 3 consecutive days.Local skin reactions were calculated at each follow up visit using a validated composite score.Efficacy was evaluated by the comparison of clinical and dermoscopic pictures before the treatment and at day 57,and classified as complete,partial and poor response.RESULTS A number of 130 patients were enrolled,of which 101(77.7%) were treated on the face,while 29 (22.3%) on the scalp.The great majority of our study population (n = 119,91.5%) reached at least a 75% clearance of AKs and,in particular,58 patients (44.6%) achieved a complete response while 61(46.9%) a partial one.Logistic backward multivariate analysis showed that facial localization,level of local skin reaction (LSR) at day 2,the highest LSR values and level of crusts at day 8 were factors independently associated with the achievement of a complete response.CONCLUSION Ingenol mebutate 0.015% gel,when properly applied,is more effective on the face than on the scalp and efficacy is directly associated to LSR score.

Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools.This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)model.The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images.Primarily,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is applied.Besides,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors.Moreover,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification model.The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model.The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset.The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.

Skin Melanoma Classification System Using Deep Learning

The deadliest type of skin cancer is malignant melanoma.The diagnosis requires at the earliest to reduce the mortality rate.In this study,an efficient Skin Melanoma Classification(SMC)system is presented using dermoscopic images as a non-invasive procedure.The SMC system consists of four modules;segmentation,feature extraction,feature reduction and finally classification.In the first module,k-means clustering is applied to cluster the colour information of dermoscopic images.The second module extracts meaningful and useful descriptors based on the statistics of local property,parameters of Generalized Autoregressive Conditional Heteroscedasticity(GARCH)model of wavelet and spatial patterns by Dominant Rotated Local Binary Pattern(DRLBP).The third module reduces the features by the t-test,and the last module uses deep learning for the classification.The individual performance shows that GARCH parameters of 3rd DWT level sub-bands provide 92.50%accuracy than local properties(77.5%)and DRLBP(88%)based features for the 1st stage(normal/abnormal).For the 2nd stage(benign/malignant),it is 95.83%(GRACH),90%(DRLBP)and 80.8%(Local Properties).The selected 2%of features from the combination gives 99.5%and 100%for 1st and 2nd stage of the SMC system.The greatest degree of success is achieved on PH2 database images using two stages of deep learning.It can be used as a pre-screening tool as it provides 100%accuracy for melanoma cases.