Dermatoscopic Images Research Articles

Discrimination between invasive and in situ melanomas using a convolutional neural network

To the Editor: Melanomas are more frequently detected by patients and not dermatologists, suggesting that the clinical diagnosis most often is uncomplicated.1McGuire S.T. Secrest A.M. Andrulonis R. Ferris L.K. Surveillance of patients for early detection of melanoma: patterns in dermatologist vs patient discovery.Arch Dermatol. 2011; 147: 673-678Google Scholar However, deciding whether a melanoma is in situ (MIS) or invasive may prove more difficult, even for expert dermatologists. A machine learning tool could potentially prove helpful for dermatologists facing this binary classification problem. Convolutional neural networks (CNNs) have recently contributed to significant advances in image classification,2Krizhevsky A. Sutskever I. Hinton G.E. Imagenet classification with deep convolutional neural networks.Commun ACM. 2017; 60: 84-90Google Scholar and several models have been designed to classify different types of skin lesions with outstanding performance in a research setting.3Esteva A. Kuprel B. Novoa R.A. et al.Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118Google Scholar,4Haenssle H.A. Fink C. Schneiderbauer R. et al.Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.Ann Oncol. 2018; 29: 1836-1842Google Scholar In this retrospective investigation, we trained a CNN using a relatively small number of dermatoscopic images. The model did not have any pretrained parameters (Supplemental material available via Mendeley at https://data.mendeley.com/datasets/y883xdgw86/1). A total of 1137 histopathologically verified lesions were included: 515 invasive (45.3%) and 622 MIS (54.7%). All images were obtained from our department from January 2016 to February 2020. The image set was randomized into 3 groups: training set (n = 749), validation set (n = 188), and test set (n = 200). The randomization preserved the proportion of invasive to MIS in each group. The model's performance was compared with that of 7 dermatologists (1 resident physician and 6 board-certified dermatologists) who independently reviewed the same test set of lesions. The model was evaluated using the test set, yielding a receiver operating characteristic (ROC) curve with an area under the curve of 0.76 (95% confidence interval [CI], 0.69-0.83). A combined assessment by the dermatologists on each case was defined by taking the mean of the individual assessments (1 = invasive, 0 = MIS), yielding a score ranging from 0 to 1. Using this score, an ROC curve for the dermatologists' combined assessment was defined, producing an area under the curve of 0.81 (95% CI, 0.75-0.87; P = .080) (Fig 1). Selecting the point where sensitivity and specificity were nearest, the CNN model correctly classified 139 out of 200 images in the test set, yielding an overall accuracy rate of 69.5% (95% CI, 62.6%-75.8%). The corresponding point on the dermatologists' ROC curve had an accuracy of 75.0% (95% CI, 68.4%-80.8%) (Fig 2). When comparing the 2 above-mentioned points for the 200 cases, the dermatologists' combined answer was accurate in 23 cases in which the CNN was wrong. The CNN's answer was accurate in 12 cases in which the dermatologists were wrong (P = .090). A limitation was that only dermatoscopic images were used. In real life, physicians integrate other metadata, including patient history and the clinical image, when determining whether a melanoma is invasive or MIS. Importantly, dysplastic nevi were not included, and the same weight was applied to misclassification of invasive melanomas and MIS. Although the training set used was relatively small, a previous investigation showed that CNN models trained on small datasets can perform well.5Fujisawa Y. Otomo Y. Ogata Y. et al.Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.Br J Dermatol. 2019; 180: 373-381Google Scholar Before a CNN can be implemented in clinical practice to aid a dermatologist in this classification problem, it needs further refinement and evaluation in a prospective setting. However, it is interesting that the performance level of the CNN model presented here was not much lower than that of the dermatologists, considering that it was trained de novo on merely 749 images. None disclosed.

Evaluation of Erythema Severity in Dermatoscopic Images of Canine Skin: Erythema Index Assessment and Image Sampling Reliability.

The regular monitoring of erythema, one of the most important skin lesions in atopic (allergic) dogs, is essential for successful anti-allergic therapy. The smartphone-based dermatoscopy enables a convenient way to acquire quality images of erythematous skin. However, the image sampling to evaluate erythema severity is still done manually, introducing result variability. In this study, we investigated the correlation between the most popular erythema indices (EIs) and dermatologists’ erythema perception, and we measured intra- and inter-rater variability of the currently-used manual image-sampling methods (ISMs). We showed that the EIBRG, based on all three RGB (red, green, and blue) channels, performed the best with an average Spearman coefficient of 0.75 and a typical absolute disagreement of less than 14% with the erythema assessed by clinicians. On the other hand, two image-sampling methods, based on either selecting specific pixels or small skin areas, performed similarly well. They achieved high intra- and inter-rater reliability with the intraclass correlation coefficient (ICC) and Krippendorff’s alpha well above 0.90. These results indicated that smartphone-based dermatoscopy could be a convenient and precise way to evaluate skin erythema severity. However, better outlined, or even automated ISMs, are likely to improve the intra- and inter-rater reliability in severe erythematous cases.

Melanoma diagnosis using deep learning techniques on dermatoscopic images

BackgroundMelanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis.MethodsAmong state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either “benign” or “malignant”.ResultsTraining, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes.ConclusionsThe results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.

Dermatoscopy of combined blue nevi: a multicentre study of the International Dermoscopy Society.

Combined blue nevi (CBN) may mimic melanoma and are relatively often biopsied for diagnostic reasons. To better characterize CBN and to compare it with melanoma. We collected clinical and dermatoscopic images of 111 histologically confirmed CBN and contrasted their dermatoscopic characteristics with 132 partly blue coloured melanomas. Furthermore, we compared the accuracy of human experts using pattern analysis with a computer algorithm based on deep learning. Combined blue nevi are usually flat or slightly elevated and, in comparison with melanoma, more frequent on the head and neck. Dermatoscopically, they are typified by a blue structureless part in combination with either brown clods (n=52, 46.8%), lines (n=28, 25.2%) or skin-coloured or brown structureless areas (n=31, 27.9%). In contrast with melanoma, the blue part of CBN is more often well defined (18.9% vs. 4.5%, P<0.001) and more often located in the centre (22.5% vs. 5.3%, P<0.001). Melanomas are more often chaotic (OR: 28.7, 95% CI: 14.8-55.7, P<0.001), have at least one melanoma clue (OR: 10.8, 95% CI: 5.2-22.2 P<0.001) in particular white lines (OR: 37.1, 95% CI: 13.4-102.9, P<0.001). Using simplified pattern analysis (chaos and clues), two raters reached sensitivities of 93.9% (95% CI: 88.4-97.3%) and 92.4% (95% CI: 86.5-96.3%) at corresponding specificities of 59.5% (95% CI: 49.7-68.7%) and 65.8% (95% CI: 56.2-74.5%). The human accuracy with pattern analysis was on par with a state-of-the-art computer algorithm based on deep learning that achieved an area under the curve of (0.92, 95% CI: 0.87-0.96) and a specificity of 85.3% (95% CI: 76.5-91.7%) at a given sensitivity of 83.6% (95% CI: 72.5-91.5%). CBN usually lack melanoma clues, in particular white lines. The accuracy of pattern analysis for combined nevi is acceptable, and histopathologic confirmation may not be necessary in exemplary cases.

Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset. The developed deep model was compared against two powerful models, i.e., InceptionV3 and VGG16, using the Dice similarity coefficient (DSC) and the area under the curve (AUC). The evaluation results show that the proposed model achieved higher results than some recent and robust models.

Study of algorithms for automated analysis of structural features of dermatoscopic images

Study of algorithms for automated analysis of structural features of dermatoscopic images

A model for the selection of structural elements of lines in digital images in oncodermatology

The problem of early diagnosis of one of the most dangerous malignant neoplasms of the skin – melanoma is considered. A model for identifying the structural elements of lines in digital images of skin neoplasms in oncodermatology has been developed. The model is based on adaptive binarization of the original digital dermatoscopic image of skin neoplasms and subsequent operations of dilation, erosion, skeletonization and filtering of false fragments of lines. Test dermatoscopic images of skin neoplasms are visually divided into four groups to conduct the experiment. The optimal parameters of image processing of four groups for the model of selection of structural elements – lines – are experimentally established. The experimentally determined accuracy of the selection of lines was 95 %. The work is the result of interdisciplinary cooperation between dermatologists of the Central Medical Academy of the Presidential Administration of the Russian Federation, the Medical Institute of the Peoples' Friendship University of Russia and specialists in the field of information and measurement systems of the Engineering and Physical Institute of Biomedicine of the National Research Nuclear University “MEPhI”. The proposed model can be used in the development of computer systems to support medical decision – making in the diagnosis of skin melanoma – a dangerous malignant neoplasm.

Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations.

Deep learning algorithms have shown excellent performances in the field of medical image recognition, and practical applications have been made in several medical domains. Little is known about the feasibility and impact of an undetectable adversarial attacks, which can disrupt an algorithm by modifying a single pixel of the image to be interpreted. The aim of the study was to test the feasibility and impact of an adversarial attack on the accuracy of a deep learning-based dermatoscopic image recognition system.First, the pre-trained convolutional neural network DenseNet-201 was trained to classify images from the training set into 7 categories. Second, an adversarial neural network was trained to generate undetectable perturbations on images from the test set, to classifying all perturbed images as melanocytic nevi. The perturbed images were classified using the model generated in the first step. This study used the HAM-10000 dataset, an open source image database containing 10,015 dermatoscopic images, which was split into a training set and a test set. The accuracy of the generated classification model was evaluated using images from the test set. The accuracy of the model with and without perturbed images was compared. The ability of 2 observers to detect image perturbations was evaluated, and the inter observer agreement was calculated.The overall accuracy of the classification model dropped from 84% (confidence interval (CI) 95%: 82–86) for unperturbed images to 67% (CI 95%: 65–69) for perturbed images (Mc Nemar test, P < .0001). The fooling ratio reached 100% for all categories of skin lesions. Sensitivity and specificity of the combined observers calculated on a random sample of 50 images were 58.3% (CI 95%: 45.9–70.8) and 42.5% (CI 95%: 27.2–57.8), respectively. The kappa agreement coefficient between the 2 observers was negative at -0.22 (CI 95%: −0.49–−0.04).Adversarial attacks on medical image databases can distort interpretation by image recognition algorithms, are easy to make and undetectable by humans. It seems essential to improve our understanding of deep learning-based image recognition systems and to upgrade their security before putting them to practical and daily use.

Artificial intelligence for melanoma diagnosis: a review of published studies until 2020

Convolutional neural networks (CNN) have shown unprecedented accuracy in digital image analysis, which can be harnessed for melanoma recognition through automated evaluation of clinical and dermatoscopic images. In experimental studies, modern CNN architectures perform single image analysis at the level of dermatologists and domain-experts, also for multi-class predictions including a multitude of possible diagnoses. This may not necessarily translate to good clinical performance, and reliable randomised controlled prospective clinical trials for modern CNNs are essentially missing. Weaknesses of CNNs are that limitations of available training image datasets propagate to limitations of CNN predictions, and they can not provide a reliable estimate of uncertainty. Recent research focuses on human-computer collaboration, where gains in accuracy were measured even with imperfect CNNs. With missing academic and clinical agreement on equivocal melanocytic lesions, fully automating histologic assessment of them with CNNs appear problematic, and applications in the near future are probably limited to supporting, referencing or recommendation roles.

Innate local response and tissue recovery following application of high density microarray patches to human skin

The development of microarray patches for vaccine application has the potential to revolutionise vaccine delivery. Microarray patches (MAP) reduce risks of needle stick injury, do not require reconstitution and have the potential to enhance immune responses using a fractional vaccine dose. To date, the majority of research has focused on vaccine delivery with little characterisation of local skin response and recovery. Here we study in detail the immediate local skin response and recovery of the skin post high density MAP application in 12 individuals receiving 3 MAPs randomly assigned to the forearm and upper arm. Responses were characterised by clinical scoring, dermatoscopy, evaporimetry and tissue viability imaging (TiVi). MAP application resulted in punctures in the epidermis, a significant transepidermal water loss (TEWL), the peak TEWL being concomitant with peak erythema responses visualised by TiVi. TEWL and TiVi responses reduced over time, with TEWL returning to baseline by 48 h and erythema fading over the course of a 7 day period. As MAPs for vaccination move into larger clinical studies more variation of individual subject phenotypic or disease propensity will be encountered which will require consideration both in regard to reliability of dose delivery and degree of inherent skin response.

Clinical, trichoscopy, and light microscopic findings in hypohidrotic ectodermal dysplasia: Report of 21 patients and a review of the literature.

Hypohidrotic ectodermal dysplasia (HED) is a genetic condition typified by alterations in skin structures including sweat glands, hair, nails, and teeth. Hair findings in HED have been poorly characterized in larger series. To characterize scalp and hair findings of patients with HED clinically and with trichoscopy and light microscopy. A cross-sectional study in 21 pediatric HED patients was performed using available clinical and scalp dermatoscopic images, as well as pulled-hair samples for clinical evaluation, trichoscopic, and light microscopic analyses. Seventeen out of 21 patients (81%) were men. Twenty patients had straight hair. Sixteen patients had decreased hair density, 6 of whom had hair loss mainly in the temporal and occipital regions. Fourteen patients had hair whorls. On trichoscopy, we observed: single-hair follicular units (n=19, 90%), scalp hyperpigmentation (n=13, 62%), variable diameter of the hair shafts (n=12, 57%), perifollicular scales (n=8, 38%), scalp erythema (n=8, 38%), and short curly pigtail hairs (n=6, 29%). On light microscopy, findings included: hair shafts with irregular diameter (n=7, 33%), heterogeneous hair color (n=6, 29%), trichoptilosis (n=2, 10%), and pili torti (n=1, 5%). In this series, hair findings in HED were similar to those described in previous studies. However, we describe two new clinical and two trichoscopic findings: decreased hair density mainly in the temporal and occipital regions, oblique upwards occipital hair follicles orientation, angled hairs, and short curly pigtail hairs. These heterogeneous findings may reflect the multiple factors and signaling pathways that can be affected in these syndromes.

Diagnostic performance study on the melanoma automated diagnosis software powered by artificial intelligence technologies

INTRODUCTION: The research evaluates a series of publications on the machine recognition efficacy of cutaneous melanoma dermatoscopic images. Some authors report high sensitivity and specificity of automated diagnostics of skin tumors. Significant differences in the published data can be attributed to the use of different algorithms and groups of skin neoplasms to calculate the accuracy rate.&#x0D; MATERIALS AND METHODS: The diagnostic performance of two automated artificial intelligence systems is compared.&#x0D; RESULTS: The convolutional neural network algorithm improves the overall diagnostic accuracy by 7% compared to the algorithm without deep learning, while the overall accuracy rate was 78%. An initial set of 100 dermatoscopic images used in the study is published online for the assessment of the applicability of the obtained data when introducing existing artificial intelligence systems.&#x0D; CONCLUSION: The main limitations and possible ways to further improve the automated diagnosis of skin tumors based on digital dermatoscopy are outlined.

A Convolutional Neural Network Framework for Accurate Skin Cancer Detection

Skin diseases have become a challenge in medical diagnosis due to visual similarities. Although melanoma is the best-known type of skin cancer, there are other pathologies that are the cause of many death in recent years. The lack of large datasets is one of the main difficulties to develop a reliable automatic classification system. This paper presents a deep learning framework for skin cancer detection. Transfer learning was applied to five state-of-art convolutional neural networks to create both a plain and a hierarchical (with 2 levels) classifiers that are capable to distinguish between seven types of moles. The HAM10000 dataset, a large collection of dermatoscopic images, were used for experiments, with the help of data augmentation techniques to improve performance. Results demonstrate that the DenseNet201 network is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. The plain model performed better than the 2-levels model, although the first level, i.e. a binary classification, between nevi and non-nevi yielded the best outcomes.

ANALYSES OF SKIN LESION AREAS AFTER THRESHOLDING

Melanoma is one of the fastest spreading cancers. The aim of the article is to segment the skin lesions from human skin dermatoscopic images covered by melanoma. Threshold segmentation was used, which allows a single skin lesion to be analyzed. It shows the four areas of each based on their color. The created software monitors the border of skin lesion areas. Segmentation and analysis of the resulting images with different areas of skin change was carried out in the Matlab software.

Algorithmic method of dermatoscopic image analysis «Prediction without pigment» and its implementation in clinical practice

The theoretical bases and practical aspects of the algorithm «Prediction without pigment» in dermatoscopic diagnostics application are considered in the article. The method was developed by a team of authors from around the world as a complement to the method of the dermatoscopic image analysis «Chaos and Clues». The «Prediction without pigment» method is an opportunity to streamline differential diagnosis of non-pigmented formations and entities with unproven pigmentation by melanin. Dermatoscopice valuation in this case is difficult, but under standing that there are someuseful tips formed the basis for the development of this technique. The formations being analyzed should not be brown or gray. Black, purpleorblue, ifpresent, shouldbeduetoblood, notmelanin. The so-called «Red Flags» help in choosing the algorithm — it is the presence of additional features or specific circumstances that characterize the formation as suspicious, and therefore require the use of algorithmic evaluation. These features can be subjective (presented by a concerned patient), or objectively visible macroscopically (clinically visible structure defect, ulcer, superficial keratin on the volumetric formation) or during dermatoscopic evaluation (polarized specific white lines).As well as «Chaos and Clues», «Prediction without pigment» should not be considered an absolute stone-carved method. It has also been developed as a useful tool suitable for seamless integration into routine practice. The authors encourage the use of it as a basis for organizing growing experience, adaptation and individualization of the method to personal style and practice.

Prospects of screening skin neoplasms in servicemen of the Armed Forces of Ukraine

Objective — to improve the methods of diagnosis of skin neoplasms and to optimize the management of patients-service­men.Materials and methods. A literature review and analysis of the results of clinical and instrumental studies of military personnel with skin neoplasms are presented.Results and discussion. Management of diagnosis and treatment of skin neoplasms is a difficult task, especially for the early detection of skin neoplasms and the provision of medical care in accordance with international recommendations and guidelines, particularly in the Armed Forces. An important issue is that the examination and management of patients with skin neoplasms are not accompanied by photodocumentation of dermatoscopic images (namely, this situation exists in the real outpatient practice of the vast majority of Ukrainian dermatovenereologists and cosmetologists) since it is difficult to compare the results of the current and previous examinations when observing a patient, especially if they conducted by different specialists. This is due to the fact that specialists do not have a standardized protocol for detection, examination and fixation of dermatoscopic signs using a certain sequence and unified terminology of their description. In addition, there are a number of issues requiring further development in relation to the provision of medical care to military patients with skin neoplasms. The Dermatological Service of Ukraine is currently in a state of reform, which significantly reduces the possibility of early patient visits to the dermatologist and, accordingly, worsens the early diagnosis of skin tumors, especially in conscripts. A complicating factor is also the insufficient regulation of the algorithm and route of the patient with skin neoplasms in order to verify or exclude their malignant nature.Conclusions. Modern views on the diagnosis and treatment of dermatovenerological neoplasms need to be changed in order to early detect skin neoplasms and provide medical care in accordance with international recommendations and guidelines, especially in the ranks of the Armed Forces of Ukraine. In Ukraine, the identification and care of military patients with skin neoplasms is an issue that needs further development.

Abstract PO-042: Would dermoscopic photographs help triage teledermatology consults in the COVID-19 era?

Abstract Background: The coronavirus pandemic led to a rapid transition to telemedicine across medical specialties. Dermatologists can utilize digital images to remotely manage skin diseases, including determining the urgency with which patients need to be seen in person. Accurately triaging urgent cases can be impacted by imaging modality. Dermoscopy is the examination of the skin with polarized light, which for some providers can increase their diagnostic accuracy and confidence in managing potential skin cancers. It has been increasingly viewed as a standard adjunct for examining skin lesions. Dermoscopic photographs have been shown to improve providers' abilities to correctly diagnose telemedicine consults. However, this has only been studied with a limited subset of skin neoplasms and is limited to a workflow involving a photographer with a dermatoscopic imaging capability. In the pandemic, the ability of patients to access dermoscopy is exceedingly limited. This study aims to assess the impact of dermoscopy on decision-making by dermatologists, including confidence, diagnostic accuracy, and triaging urgency. Our premise is that if dermoscopy is impactful, innovative means to increase accessibility to dermoscopy would be valuable for cancer management during COVID. Methods: Twenty sets of clinical and dermoscopic photographs were selected as representative of commonly seen skin pathologies in our teledermatology consults. Study participants were first shown a clinical photograph and asked to determine their diagnoses, management decisions, level of urgency, and confidence in their triage decisions. The responses were scaled ranging from non-neoplastic to malignant, 0-100% confidence, and non-urgent (1) to emergent (3), respectively. They were then asked to answer the same questions after viewing the associated dermoscopic image. Results: Twenty-six physicians participated in the study: 16 dermatology attendings and 10 residents. The majority reported using dermoscopy in ≥25% of their clinical practice. Fifty-nine percent rated themselves as "somewhat confident" in their dermoscopic abilities. Providers correctly diagnosed 45.3% of study cases using clinical images alone. This increased to 53.6% after viewing the associated dermoscopic images (p=0.02). The greatest increase was for malignant neoplasms (31% vs. 54%, p=0.0007). Dermoscopy significantly reduced triage urgency scores for both non-neoplastic (mean 1.6 vs. 1.2, p&amp;lt;0.001) and benign neoplastic (mean 1.43 vs. 1.35, p=0.01) pathologies. Dermoscopy significantly increased urgency scores for malignant neoplasms (mean 1.47 vs. 1.64, p=0.01). There was a 7.6% increase in providers' confidence in their management decisions with dermoscopy (p&amp;lt;0.0001). Conclusions: Dermoscopic photographs improve providers’ abilities to correctly diagnose and prioritize skin lesions. In the COVID-19 era, innovative means to make dermoscopy available to patients at risk for skin cancer would be valuable. Citation Format: Myles R. McCrary, Tova Rogers, Howa Yeung, Loren Krueger, Suephy C. Chen. Would dermoscopic photographs help triage teledermatology consults in the COVID-19 era? [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-042.

Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis

Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis

Skin Cancer Detection using CNN Algorithm

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

The effects of skin lesion segmentation on the performance of dermatoscopic image classification

Background and ObjectiveMalignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown excellent classification and segmentation performances for dermatoscopic skin lesion images. These models can be trained end-to-end without requiring any hand-crafted features. However, the effect of using lesion segmentation information on classification performance has remained an open question. MethodsIn this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification. To do this, first, we developed a baseline classifier as the reference model without using any segmentation masks. Then, we used either manually or automatically created segmentation masks in both training and test phases in different scenarios and investigated the classification performances. The different scenarios included approaches that exploited the segmentation masks either for cropping of skin lesion images or removing the surrounding background or using the segmentation masks as an additional input channel for model training. ResultsEvaluated on the ISIC 2017 challenge dataset which contained two binary classification tasks (i.e. MM vs. all and seborrheic keratosis (SK) vs. all) and based on the derived area under the receiver operating characteristic curve scores, we observed four main outcomes. Our results show that 1) using segmentation masks did not significantly improve the MM classification performance in any scenario, 2) in one of the scenarios (using segmentation masks for dilated cropping), SK classification performance was significantly improved, 3) removing all background information by the segmentation masks significantly degraded the overall classification performance, and 4) in case of using the appropriate scenario (using segmentation for dilated cropping), there is no significant difference of using manually or automatically created segmentation masks. ConclusionsWe systematically explored the effects of using image segmentation on the performance of dermatoscopic skin lesion classification.