Different Types Of Skin Lesions Research Articles

ENHANCE SKIN CANCER DEEP LEARNING-BASED CATEGORIZATION: A THOROUGH EXAMINATION AND COMPARATIVE STUDY

Abstract: One of the most prevalent types of cancer in the world is skin cancer, which includes melanoma, squamous cell carcinoma, and basal cell carcinoma. While increasing survival rates requires early discovery and treatment, conventional diagnostic techniques frequently have subjectivity and inconsistent results. The purpose of this study is to better understand how deep learning techniques, namely convolutional neural networks (CNNs), might improve the detection of skin cancer. Using the ISIC dataset, eight advanced models—DenseNet121, InceptionV3, ResNet50V2, VGG16, VGG19, InceptionResNetV2, Xception, and CNN—were trained and verified to categorize different types of skin lesions. The outcomes highlight the superior accuracy that models like as Xception and InceptionV3 can accomplish, highlighting their potential for accurate and timely detection. This investigation assesses how well different deep learning models diagnose different skin diseases. Models like Densenet121, InceptionV3, and VGG16 are compared based on metrics like accuracy, recall, precision, and F1-score. When it comes to multi-classification, InceptionResNetV2 and VGG16 stand out as the best performers with 0.9547 accuracy, while Custom CNN performs the worst with 0.8040. A thorough comparison of the accuracy, precision, recall, and F1-score performance indicators identifies the unique advantages and disadvantages of each model. This study demonstrates how artificial intelligence (AI) is revolutionizing medical imaging by offering a reliable, unbiased, and highly accurate method for detecting skin cancer. The integration of deep learning models into clinical practice is made possible by these discoveries, which might enhance patient outcomes and diagnostic accuracy in dermatology. IndexTerms – Python, Machine Learning, Deep Learning, Skin Cancer, Deep Learning Algorithms, Medical Imaging, Neural Network Models.

Histopathological spectrum of skin lesions in a tertiary health care centre – A retrospective study

Skin is the body’s largest and most complex organ, performing a wide range of functions. Environmental factors, genetics, cleanliness standards and social norms all influence the type of skin lesions that exist in different countries and regions. The skin lesions can vary from inflammatory to neoplastic. In clinically alike dermatoses, skin biopsy is used to provide significant information to the pathologist and dermatologist. The aim of the study is to identify and discuss the histopathology of different types of skin lesions, as well as their prevalence, age, sex distribution and location of lesions among hospital patients and compare the results with other relevant studies. The study was a retrospective observational study undertaken for the duration of three years in the Pathology Department of a tertiary health care center. Skin biopsy slides stained with hematoxylin and eosin were studied and classified after a detailed histopathological study under light microscope. 178 cases of skin lesions in total were evaluated having a 1.5:1 male to female ratio. Nonneoplastic lesions constituted 30.33% of skin lesions, while benign and malignant tumors constituted 55.05% and 14.6% of skin lesions respectively. In neoplastic lesions, keratinocytic tumors were the most common tumor type. The most common malignant tumor was squamous cell carcinoma, whereas epidermal inclusion cyst was the most prevalent benign tumor. The head and neck were the most prevalent sites for skin lesions. Most of the skin lesions can be accurately diagnosed by histopathological evaluation of biopsy of lesions in combination with clinical findings.

Human Skin Disease Detection Using Machine Learning

Abstract: In this research, I want to improve deep convolution neural networks that have been successful with the ImageNet dataset at categorizing seven different types of skin lesions using the HAM10000 dataset, which has 10,000 dermatoscopic pictures. With VGG16, Inception V3, Inception ResNet V2, and Dense Net 201, the top layers were fine-tuned.

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background:Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Objective:Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. Methods:We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analyzed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. Results:The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. Conclusions:To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilized in real-time.

Conditions and Factors That Raise the Risk of Developing Skin Lesions After Shingles.

To understand the situation and risk factors of skin lesions following the eruption of shingles. We selected 275 patients with shingles who had been diagnosed and treated in the Dermatology Department of Changshu No. 1 People's Hospital between July 2017 and March 2022. Age, gender, skin lesion site, skin lesion type, prodromal pain, history of diabetes, history of hypertension, history of other immune diseases, as well as other pertinent clinical data, were collected. The severity and pain of patients with severe shingles were evaluated, and their fasting blood sugar and plasma albumin were measured for routine antiviral treatment. They were followed up 6 months-the types of skin lesions and pertinent clinical data were compared, and the risk factors for skin lesions were analyzed. There were no statistically significant differences in gender, age, or site among the different types of skin lesions (P > 0.05). The severity of skin lesions, acute pain, history of diabetes, history of scars, low immune function, combined with hypoproteinemia, squeezing and stripping behavior, and post-herpetic neuralgia (PHN) were significantly associated with skin lesions (P < 0.05). The results of multivariate analysis showed that: age ≥60 years old, severe skin injury combined with diabetes, low immune function, scar history, squeezing and stripping were independent risk factors for the development of skin lesions due to shingles. There is no significant difference in age, gender, site, or other characteristics between the types of skin lesions due to shingles. The independent risk factors of skin lesions due to shingles are old age, severe rash, history of scars, diabetes, low immunity, squeezing, and peeling.

A Series of 35 Cutaneous Infections Caused by Mycobacterium marinum in Han Chinese Population.

Cutaneous Mycobacterium marinum infection is an increasingly infectious disease presenting unique diagnostic and therapeutic challenges. The aim of this study was to evaluate the differences in time to treatment among patients with different types of skin lesions and who were treated with single or multidrug therapies. In addition, the clinical characteristics of M. marinum infection were explored and the mechanism of the host immune responses was investigated. The electronic medical records of 35 patients with M. marinum infection were reviewed. The clinical characteristics, histopathological and laboratory data, and treatment outcomes were analyzed. Immunohistochemical analysis was performed to clarify the immune mechanisms induced by M. marinum infection in 9 patients and 5 healthy controls. Of the 35 patients, 25 (71.4%) had lesions with sporotrichoid patterns. The duration of patients with sporotrichoid lesions or treatment with multiple drugs was longer, although differences were not significant, possibly due to the small cohort. However, this trend was also observed in previous studies, making it worthy of further attention. Expression levels of cytokines (IFN-γ, IL-4, IL-9, and FOXP3) were significantly upregulated in the patient specimens, whereas there were no significant differences in IL-17 and IL-22 expression levels between the patient and control groups.

Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning

AbstractA key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.

Skin Melanoma Cancer Detection and Classification using Machine Learning

Skin cancer is a common and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate classification of skin lesions are critical for effective therapy and improved patient outcomes. In recent years, advances in machine learning and computer vision techniques have shown promising results in automating skin cancer detection and classification. The goal of this project is to develop an automated system for skin cancer detection and classification using machine learning algorithms. The proposed system uses a dataset of dermatoscopic images collected from different sources covering different types of skin lesions, including malignant melanoma, basal cell carcinoma, and squamous cell carcinoma. The project includes several main stages. First, preprocessing techniques including noise reduction, normalization, and feature extraction are used to improve image quality. Next, a comprehensive set of features such as color, texture, and portrait features are extracted from the preprocessed images. These features are input to various machine learning models, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs) and Random Forests.

Micro-relief characterization of benign and malignant skin lesions by polarization speckle analysis in vivo.

A recent direction in skin disease classification is to develop quantitative diagnostic techniques. Skin relief, colloquially known as roughness, is an important clinical feature. The aim of this study is to demonstrate a novel polarization speckle technique to quantitatively measure roughness on skin lesions in vivo. We then calculate the average roughness of different types of skin lesions to determine the extent to which polarization speckle roughness measurements can be used to identify skin cancer. The experimental conditions were set to target the fine relief structure on the order of ten microns within a small field of view of 3 mm. The device was tested in a clinical study on patients with malignant and benign skin lesions that resemble cancer. The cancer group includes 37 malignant melanomas (MM), 43 basal cell carcinomas (BCC), and 26 squamous cell carcinomas (SCC), all categories confirmed by gold standard biopsy. The benign group includes 109 seborrheic keratoses (SK), 79 nevi, and 11 actinic keratoses (AK). Normal skin roughness was obtained for the same patients (301 different body sites proximal to the lesion). The average root mean squared (rms) roughness ± standard error of the mean for MM and nevus was equal to 19 ± 5 μm and 21 ± 3 μm, respectively. Normal skin has rms roughness of 31 ± 3 μm, other lesions have roughness of 35 ± 10 μm (AK), 35 ± 7 μm (SCC), 31 ± 4 μm (SK), and 30 ± 5 μm (BCC). An independent-samples Kruskal-Wallis test indicates that MM and nevus can be separated from each of the tested types of lesions, except each other. These results quantify clinical knowledge of lesion roughness and could be useful for optical cancer detection.

HISTO-PATHOLOGICAL SPECTRUM OF SKIN LESIONS IN TERTIARY CARE CENTRE: A THREE YEAR STUDY

Introduction: The largest sensory organ of human body is the skin, which accounts for about 15% of total body weight. It is a complex organ having multiple functions. Primarily it acts as a barrier against various harmful environmental agents (1).Skin diseases affect all age groups and are much common in developing countries. In the field of dermatology, 2000 different skin diseases are well known(2) Objectives: The objectives of the study were as follows: (1) To estimate the prevalence of various non-neoplastic and neoplastic skin lesions in our tertiary care hospital. (2) To study the age and sex-wise distribution of non-neoplastic and neoplastic skin lesions. Methods: The present study comprised a total of 102 skin biopsies of non-neoplastic lesions and neoplastic skin lesions, relevant data like age, sex, and sites of skin lesion were obtained from request forms provided to histopathology section. Specimens received were fixed in 10% formalin. Biopsy bits were submitted as whole and processed in automated tissue processor for routine paraffin embedding. Results: Out of 102 biopsies that were analyzed, most belonged to 21- 60years with slight male predominance with a total of 85 cases being Non-Neoplastic skin lesions, of which Chronic dermatitis 18 (28.5%) was the most common histopathological diagnosis, followed by Hansens disease 21(24.7%) among granulomatous skin disease, and a total of 17 cases of neoplastic skin lesions were identified of which dermatofibroma 2(11.8%) and eccrine spiradenoma 2(11.8%) were the benign variants , and Squamous cell carcinoma 3(17.6%) was the most common malignant variant. Conclusion: Histopathological evaluation of skin biopsies holds gold standard technique for diagnosing various skin lesions and their microscopic evaluation helps in assessing variegated morphological features of different types of skin lesions.

067 Eruptive lichen planus associated with the chronic lymphocytic leukemia: a case report

Chronic lymphocytic leukemia (CLL) is a malignant lymphoproliferative disease characterized by the accumulation of immature monoclonal lymphocytes in blood cells, bone marrow, spleen and lymph nodes. Patients with leukemia manifest different types of skin lesions that occur in about 25% of cases. These lesions can be divided into a) specific lesions, b) cutaneous manifestations closely related to the disease but without a leukemoid infiltrate and c) associations with various dermatologic conditions.

Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience.

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma.

Enhanced deep bottleneck transformer model for skin lesion classification

Background and objectives:Skin cancer is the most common cancer worldwide, and therein the malignant melanoma may lead to less than 5-year life expectancy. Via early-stage detection and recognition, even the deadliest melanoma can be cured to greatly increase the patient’s survival rate. Recently dermoscopy imaging is capable of capturing high-resolution magnified images of the infected skin region to automatic lesion classification, and deep learning network has been witnessed great potential of accurately recognizing different types of skin lesions. This study aims to exploit a novel deep model to enhance the skin lesion recognition performance. Methods:In spite of the remarkable progress, the existing deep network based methods naively deploy the proposed network architectures in generic image classification to the skin lesion classification, and there has still large space for performance improvement. This study presents an enhanced deep bottleneck transformer model, which incorporates self-attention to model the global correlation of the extracted features from the conventional deep models, for boosting the skin lesion performance. Specifically, we exploit an enhanced transformer module via incorporating a dual position encoding module to integrate encoded position vector on both key and query vectors for balance learning. By replacing the bottleneck spatial convolutions of the late-stage blocks in the baseline deep networks with the enhanced module, we construct a novel deep skin lesion classification model to lift the skin lesion classification performance. Results:We conduct extensive experiments on two benchmark skin lesion datasets: ISIC2017 and HAM10000 to verify the recognition performance of different deep models. The three quantitative metrics of accuracy, sensitivity and specificity on the ISIC2017 dataset with our method reach to 92.1%, 90.1% and 91.9%, respectively, which manifests very good balance result between the sensitivity and specificity, while the results on the accuracy and precision for the HAM10000 dataset are 95.84% and 96.1%. Conclusions:Results on both datasets have demonstrated that our proposed model can achieve superior performance over the baseline models as well as the state-of-the-art methods. This superior results using the incorporated model of the transformer of convolution module would inspire further research on the wide application of the transformer-based block for the real scenario without large-scale dataset.

Chlormethine Gel is Efficient and Safe in Mycosis Fungoides Skin Lesions.

Chlormethine is a bifunctional cytotoxic alkylating agent that binds to DNA, resulting in cell death (apoptosis). Chlormethine (also known as mechlorethamine) gel (CL gel) was approved in the European Union in 2017 and was first used in 2019. The aim of the study is to examine evidence regarding the efficacy and safety of chlormethine gel in everyday clinical experience from a cutaneous lymphoma centre. Twenty-three patients with stage IA–IIB mycosis fungoides received chlormethine gel between September 2020 and May 2021. All patients started by applying the gel daily and were monitored every month. At 1, 3, 6 and 9 months, 0%, 43.47%, 56.52% and 65.22% of patients, respectively, achieved an overall response. Five out of 23 patients (21.73%) achieved near complete response at a mean time of 6 months. Chlormethine gel was given as monotherapy in 12 patients (52.17%), and in addition to systemic treatments (methotrexate and peginterferon alpha-2a) in 11 patients (47.82%). Adverse events (AE) were recorded in 43.47% of patients, but only 3 discontinued treatment, due to dermatitis. Scale down of the treatment to application 3-times per week led to better patient compliance. This study shows that chlormethine gel is effective and safe in patients with mycosis fungoides with different types of skin lesions.

Different Types of Skin Lesions in Multicentric Castleman Disease.

Different Types of Skin Lesions in Multicentric Castleman Disease.

The Biochemical and Molecular Analysis of Changes in Melanogenesis Induced by UVA-Activated Fluoroquinolones-In Vitro Study on Human Normal Melanocytes.

Fluoroquinolones cause phototoxic reactions, manifested as different types of skin lesions, including hyperpigmentation. The disturbances of melanogenesis indicate that fluoroquinolones may affect cellular processes in melanocytes. It has been reported that these antibiotics may bind with melanin and accumulate in pigmented cells. The study aimed to examine the changes in melanogenesis in human normal melanocytes exposed to UVA radiation and treated with lomefloxacin and moxifloxacin, the most and the least fluoroquinolone, respectively. The obtained results demonstrated that both tested fluoroquinolones inhibited melanogenesis through a decrease in tyrosinase activity and down-regulation of tyrosinase and microphthalmia-associated transcription factor production. Only lomefloxacin potentiated UVA-induced melanogenesis. Under UVA irradiation lomefloxacin significantly enhanced melanin content and tyrosinase activity in melanocytes, although the drug did not cause an increased expression of tyrosinase or microphthalmia-associated transcription factor. The current studies revealed that phototoxic activity of fluoroquinolones is associated with alterations in the melanogenesis process. The difference in phototoxic potential of fluoroquinolones derivatives may be connected with various effects on UVA-induced events at a cellular level.

Bio-P-10 - Evaluation of the role of different cell populations in mycosis fungoides microenvironment as a tool for biomarker identification for disease progression and individualized therapy

<h3>Introduction and objectives:</h3> Tumor microenvironment constitutes a promising area for the investigation of mechanisms involved in the development, progression, and drug responses in MF-CTCL. Mass cytometry (CyTOF) analysis gives a holistic view of different cell populations on skin tumor microenvironment by simultaneous identification and enumeration of over 37 immune cell subsets providing more information from other already established techniques. This study is focused on the evaluation of MF-CTCL patients' immune profile, in different types of skin lesions or sequential analysis of patients' skin lesions. <h3>Materials and methods:</h3> 14 MF-CTCL patients enrolled in this study. 4 patients with IA stage, 5 patients with IB stage, 5 patients IIB stage. Single cells were isolated from skin biopsies as follows: 2 samples from patch, 18 samples from plaques, and 7 samples from tumors. Sequential pre-, during and post-treatment biopsies were obtained from 3 patients, whereas biopsies before and after treatment were obtained from 2 patients. Finally, paired samples (plaque and tumors) were obtained from 5 patients. Five skin biopsies deriving from patients with non-malignant skin lesions were used as control. Single cells from 6mm punch biopsies were processed for CyTOF analysis using the Maxmar Direct Immune Profiling Assay for identification and enumeration of CD4+ and CD8+ (naive, central memory, effector memory, and terminal effector), CD4+ regulatory T cells, CD4–mucosal-associated invariant T cells (MAIT)/natural killer T (NKT) cells, B cell subsets (naive and memory, plasmablasts), natural killer (early and late) cells, T helper (Th) cell phenotypic subsets (Th1-like, Th2-like, and Th17-like), gamma delta (γδ) T cells, monocytes (classical, transitional, and nonclassical), dendritic cell subsets (plasmacytoid and myeloid), granulocytes, basophils, eosinophils, and neutrophils. We also determined the expression of 6 additional markers (PD-1, PD-L1, OX40, CD7, CD26, CD206). <h3>Results:</h3> We identified different cell subpopulations, either of malignant clones or immune cells of the microenvironment, providing evidence for the single-cell heterogeneity factors that are responsible for the heterogeneity in patients with MF at different disease stages. <h3>Conclusions:</h3> The heterogeneity that characterizes different stages of MF, the intra-individual and inter-individual differences, necessitates the application of this new "state of the art" technology (CyTOF) to provide holistic consideration of malignancy in correlation with the microenvironment, and it can be considered as a tool for precision and personalized therapeutic approach.

Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet.

Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. This article presents an approach that uses CNNs with a NASNet architecture to recognize in a more accurate way, without segmentation, eight skin diseases. The model was trained end-to-end on Keras with augmented skin diseases images from the International Skin Imaging Collaboration (ISIC). The CNN architectures were initialized with weight from ImageNet, fine-tuned in order to discriminate well among the different types of skin lesions, and then 10-fold cross-validation was applied. Finally, some evaluation metrics are calculated as accuracy, sensitivity, and specificity and compare with other CNN trained architectures. This comparison shows that the proposed system offers higher accuracy results, with a significant reduction on the training paraments. To the best of our knowledge and based in the state-of-art recompiling in this work, the application of the NASNet architecture training with skin image lesion from ISIC archive for multi-class classification and evaluated by cross-validation, represents a novel skin disease classification system.

Multi-class segmentation of skin lesions via joint dictionary learning

Melanoma is the deadliest type of human skin cancer. However, it is curable if diagnosed in an early stage. Recently, computer aided diagnosis (CAD) systems have drawn much interests. Segmentation is a crucial step of a CAD system. There are different types of skin lesions having high similarities in terms of color, shape, size and appearance. Most available works focus on a binary segmentation. Due to the huge variety of skin lesions and high similarities between different types of lesions, multi-class segmentation is still a challenging task. Here, we propose a method based on joint dictionary learning for multi-class segmentation of dermoscopic images. The key idea is based on combining data from different feature spaces to build a more informative structure. We consider training data from two different spaces. Then, two dictionaries are jointly learned using the K-SVD algorithm. The final segmentation is accomplished by a graph-cut method based on both the topological information of lesions and the learned dictionaries. We evaluate our proposed method on the ISIC 2107 dataset to segment three classes of lesions. Our method achieves better results, specially for challenging skin lesions, compared to the only available method for multi-class segmentation of dermoscopic images. We also evaluate the performance of our method for binary segmentation and lesion diagnosis and compared the results with the other state-of-the-art methods. Experimental results show the efficiency and effectiveness of the proposed method in producing results that are more reliable for clinical applications, even using limited amount of training data.

Quantifying acceptable artefact ranges for dermatologic classification algorithms.

BackgroundMany classifiers have been developed that can distinguish different types of skin lesions (e.g., benign nevi, melanoma) with varying degrees of success.1–5 However, even successfully trained classifiers may perform poorly on images that include artefacts. While problems created by hair and ink markings have been published, quantitative measurements of blur, colour and lighting variations on classification accuracy has not yet been reported to our knowledge.ObjectivesWe created a system that measures the impact of various artefacts on machine learning accuracy. Our objectives were to (1) quantitatively identify the most egregious artefacts and (2) demonstrate how to assess a classification algorithm's accuracy when input images include artefacts.MethodsWe injected artefacts into dermatologic images using techniques that could be controlled with a single variable. This allows us to quantitatively evaluate the impact on the accuracy. We trained two convolutional neural networks on two different binary classification tasks and measured the impact on dermoscopy images over a range of parameter values. The area under the curve and specificity‐at‐a‐given‐sensitivity values were measured for each artefact induced at each parameter.ResultsGeneral blur had the strongest negative effect on the melanoma versus other task. Conversely, shifting the hue towards blue had a more pronounced effect on the suspicious versus follow task.ConclusionsClassifiers should either mitigate artefacts or detect them. Images should be excluded from diagnosis/recommendation when artefacts are present in amounts outside the machine perceived quality range. Failure to do so will reduce accuracy and impede approval from regulatory agencies.